Onvexia: Crypto Fundamentals, Sentiment & Onchain Tracking

Official66 tools

by faraz152 · Python

Crypto fundamentals, sentiment, whale tracking and influencer call accuracy for traders and agents.

com.onvexia/onvexia MCP Server

The com.onvexia/onvexia Model Context Protocol (MCP) server provides crypto fundamental analysis, sentiment, and whale tracking intended for use by traders and AI agents. It focuses on on-chain analytics and related research topics across major assets and the broader altcoin market.

🛠️ Key Features

Crypto fundamental analysis
Sentiment analysis
Whale tracking / on-chain whale signals

🚀 Use Cases

Crypto research for trading decisions
Feeding sentiment and whale-tracking context into AI agents
Market monitoring across bitcoin, ethereum, and altcoins

⚡ Developer Benefits

MCP integration for model-context workflows
Tooling oriented around fundamental-analysis and tokenomics topics
Supports topics spanning crypto, onchain-analytics, and crypto-research

⚠️ Limitations

Server capabilities are described only at a high level (fundamental analysis, sentiment, whale tracking); no specific tool details are provided.

Topics

ai-agentsbitcoinchatgptclaudecryptocryptocurrencyethereummcpmcp-servermodel-context-protocolonchain-analyticssentiment-analysiswhale-trackingx402altcoinscrypto-researchcrypto-tradingtradingfundamental-analysistokenomics

Captured live from the server via tools/list.

get_asset

An asset's core profile by symbol: name, category and cross-system identifiers (CoinGecko id, contract addresses, chains). Start here when you have a ticker and need to be sure which asset it refers to. Tickers collide across chains — if the symbol is ambiguous, resolve_ticker is the tool that says so instead of guessing.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_assetArguments"
}

get_asset_scores

An asset's Galaxy Score and AltRank, with the components behind each. Galaxy Score is a composite of social and market health on a 0-100 scale; AltRank is relative standing against the rest of the universe, where 1 is best. READ THE COMPONENT BREAKDOWN — a score moved by sentiment and one moved by volume mean different things, and the composite alone cannot tell you which happened.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_asset_scoresArguments"
}

get_asset_fundamentals

Get the full fundamental brief for an asset: market snapshot, supply and valuation, project, TVL, revenue, treasury, security, governance, unlock schedule, derivatives positioning, competitive rank, valuation ratios and a graded scorecard — in one call. READ THE SECTION STATES, NOT ONLY THE VALUES. Each section is `measured`, `not_held` or `failed`, and sections the asset class cannot have are returned separately in `not_applicable`. "This chain has no DAO treasury" and "we could not read it" are different facts and this response keeps them apart. Revenue is split: `S06` is the entity's own fees, `S06b` is the total earned by protocols deployed on a chain. The two can differ by two orders of magnitude and only the first accrues to the token.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_asset_fundamentalsArguments"
}

get_asset_technicals

Get support and resistance merged across 1w/1d/4h/1h, per-timeframe indicators, and derived spot/long/short setups for an asset. Each level carries the timeframes that confirmed it and the method on each — a level agreed by four charts is a different claim from one seen on the hourly. `measured_against` names the exchange and pair every distance was computed from, and `price_age_minutes` says how old that price is. Setups are GEOMETRY, not forecasts: an entry is a level cluster, a stop is that level offset by a measured multiple of daily range, and `rr_ratio` is computed from those prices. `status` is derived per request — pending, in_zone or passed.

Parameters2

symbol	string	required
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    },
    "limit": {
      "default": 8,
      "title": "Limit",
      "type": "integer"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_asset_technicalsArguments"
}

get_top_galaxy_scores

The assets with the strongest Galaxy Score right now. Galaxy Score is 0-100 and composite: social volume, engagement, sentiment and market health folded together. A high score is a statement about ATTENTION AND HEALTH, not about valuation — it does not mean an asset is cheap. Call get_asset_scores for the breakdown.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 10,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_top_galaxy_scoresArguments"
}

get_top_altranks

The assets ranked best by AltRank right now — relative standing, not absolute. AltRank is a RANK: 1 is the strongest in the universe. A rising AltRank in a falling market means outperforming the fall, not going up. Pair with get_asset_scores when the distinction matters.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 10,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_top_altranksArguments"
}

get_correlation

How closely an asset's social activity tracks its price, with the lead/lag. Correlation is not causation and this endpoint does not claim it is. A high coefficient says the two series moved together over the window — it does not say which one moved first. Use get_leading_indicators for that question.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_correlationArguments"
}

get_leading_indicators

Which social and on-chain signals have historically MOVED FIRST for an asset. This is the lead/lag question that get_correlation deliberately does not answer. A lead measured over a past window is not a forecast and the response does not present it as one — it is the observed ordering of two series, and it can break.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_leading_indicatorsArguments"
}

get_social_dominance

Each asset's SHARE of total social attention over a window, with the posts, distinct authors, engagement and sentiment behind the share. Share is relative and sums across the universe, so an asset's dominance can fall while its absolute volume rises — that is the market getting louder, not the asset getting quieter. Distinct authors is the column that separates a real conversation from one account posting 400 times.

Parameters1

hours

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "hours": {
      "default": 168,
      "title": "Hours",
      "type": "integer"
    }
  },
  "title": "get_social_dominanceArguments"
}

get_topic_rank

Rank what the market is TALKING ABOUT — themes and narratives, not assets. Ranked by mentions weighted by engagement over the window, so a topic posted about loudly by few accounts does not outrank one discussed widely. Use get_trending_assets for tickers; this is the layer above, where "restaking" and "AI agents" live.

Parameters2

hours	integer	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "hours": {
      "default": 168,
      "title": "Hours",
      "type": "integer"
    },
    "limit": {
      "default": 10,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_topic_rankArguments"
}

get_whale_transactions

Recent large on-chain transfers, optionally filtered to one asset. "Whale" is a SIZE threshold, not an identity. A large transfer is very often an exchange moving its own funds between wallets, which is not a market action at all — counterparty labels are included where we hold them, and a null label means WE HAVE NO LABEL, never that the counterparty is unknown or safe.

Parameters2

limit	integer	optional
symbol	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 25,
      "title": "Limit",
      "type": "integer"
    },
    "symbol": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Symbol"
    }
  },
  "title": "get_whale_transactionsArguments"
}

get_exchange_flows

Net movement of an asset into and out of exchange wallets. Inflows are supply arriving somewhere it can be sold; outflows are supply leaving to self-custody. The conventional reading is distribution vs accumulation, but a single large transfer can be an exchange rebalancing its own wallets — check get_entity_flows before attributing intent.

Parameters1

symbol

any

optional

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Symbol"
    }
  },
  "title": "get_exchange_flowsArguments"
}

get_aspect_sentiment

Split an asset's sentiment by what people are actually talking about: technology, price, team and community. The aggregate can be flat while the parts disagree sharply — bullish on technology, bearish on team is a different situation from uniformly neutral, and only this tool can tell them apart.

Parameters1

asset

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "asset": {
      "title": "Asset",
      "type": "string"
    }
  },
  "required": [
    "asset"
  ],
  "title": "get_aspect_sentimentArguments"
}

analyze_sentiment

Score any text for crypto sentiment, tuned for crypto slang and tickers. Takes arbitrary text you supply — it does not look anything up. General sentiment models read "this is going to zero" and "wagmi" badly; this one is fitted to the register. Returns polarity plus the terms that drove it, so a score can be checked rather than trusted.

Parameters1

text

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "text": {
      "title": "Text",
      "type": "string"
    }
  },
  "required": [
    "text"
  ],
  "title": "analyze_sentimentArguments"
}

get_influencers

The accounts driving conversation about one asset, by reach and engagement. Ranked by measured activity in our corpus, NOT by follower count, and NOT by whether they were right — see get_influencer_ledger for track record. A large account posting noise ranks here; that is the point of keeping the two tools separate.

Parameters1

asset

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "asset": {
      "title": "Asset",
      "type": "string"
    }
  },
  "required": [
    "asset"
  ],
  "title": "get_influencersArguments"
}

get_signal_integrity

Get the Signal Integrity score (0-100) — is the move real or exit liquidity? Fuses social authenticity + on-chain reality + fundamental backing, with an Exit Liquidity Radar flag.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_signal_integrityArguments"
}

get_influencer_ledger

Get an influencer's track record. `view` picks one of three records: accountability (default) — did their calls precede the move (Predictor) or react to it (Reactor)? Hit rate and track record. accuracy — did their price calls come true? Hit rate over 30/90/180 days at 24h, 7d and 30d, with the Wilson lower bound and excess over BTC. calls — the receipts: each call's post excerpt and link, entry price, and what happened after 24h/7d/30d. `horizon_hours` (24, 168, 720) picks which outcome the list is sorted and filtered on. One request per call. `influencer_id` is a creator id or username.

Parameters3

influencer_id	string	required
view	string	optional
horizon_hours	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "influencer_id": {
      "title": "Influencer Id",
      "type": "string"
    },
    "view": {
      "default": "accountability",
      "title": "View",
      "type": "string"
    },
    "horizon_hours": {
      "default": 168,
      "title": "Horizon Hours",
      "type": "integer"
    }
  },
  "required": [
    "influencer_id"
  ],
  "title": "get_influencer_ledgerArguments"
}

get_influencer_leaderboard

The influencer leaderboard, ranked by what people actually got RIGHT rather than by how loud they are. rank_by="accountability" (default): scored Predictor vs Reactor — did the call come before the move, or after it. rank_by="accuracy": ranked by how often their price calls came true, by the Wilson lower bound of the hit rate (>= 20 resolved calls to rank), over `window_days` (30/90/180) judged at `horizon_hours` (24/168/720). Reach and accuracy are different axes, and most rankings only publish the first.

Parameters3

rank_by	string	optional
window_days	integer	optional
horizon_hours	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "rank_by": {
      "default": "accountability",
      "title": "Rank By",
      "type": "string"
    },
    "window_days": {
      "default": 90,
      "title": "Window Days",
      "type": "integer"
    },
    "horizon_hours": {
      "default": 168,
      "title": "Horizon Hours",
      "type": "integer"
    }
  },
  "title": "get_influencer_leaderboardArguments"
}

search_assets

Search the whole asset universe (~1,900 assets) by symbol or name. Use this to resolve a user's loose reference into a real symbol before calling the other tools.

Parameters2

query	string	required
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "title": "Query",
      "type": "string"
    },
    "limit": {
      "default": 20,
      "title": "Limit",
      "type": "integer"
    }
  },
  "required": [
    "query"
  ],
  "title": "search_assetsArguments"
}

get_data_coverage

What data this platform actually holds right now: asset count, how many are priced, chains covered, labelled addresses, social corpus size, and freshness timestamps. Call this to check whether an answer is supportable before asserting it.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_data_coverageArguments"
}

get_chain_coverage

Per-chain coverage — tokens mapped, labelled addresses, whale transactions seen, and how many were attributed to a named entity. Attribution is Etherscan-derived, so it is strong on Ethereum and sparse on other chains.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_chain_coverageArguments"
}

get_asset_platforms

Every chain an asset is deployed on, with its contract address and token decimals.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_asset_platformsArguments"
}

lookup_address

Identify a blockchain address — exchange, bridge, DEX, MEV bot, mining pool, or OFAC-sanctioned — with the source and confidence of each label. IMPORTANT: `known: false` means no label is held. It does NOT mean the address is clean or unflagged.

Parameters1

address

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "address": {
      "title": "Address",
      "type": "string"
    }
  },
  "required": [
    "address"
  ],
  "title": "lookup_addressArguments"
}

list_labelled_addresses

Parameters4

chain	string	optional
entity	string	optional
category	string	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "chain": {
      "default": "",
      "title": "Chain",
      "type": "string"
    },
    "entity": {
      "default": "",
      "title": "Entity",
      "type": "string"
    },
    "category": {
      "default": "",
      "title": "Category",
      "type": "string"
    },
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "list_labelled_addressesArguments"
}

list_metrics

List every metric Onvexia knows, with its parity and caveats. parity=exact means we compute it the way Santiment does; approximate means same concept but different coverage or method (read the caveat before relying on the number); unavailable means we do NOT serve it yet. Unavailable metrics are listed on purpose — check here before asserting that Onvexia can answer a question.

Parameters2

category	string	optional
available_only	boolean	optional

Raw schema

{
  "type": "object",
  "properties": {
    "category": {
      "default": "",
      "title": "Category",
      "type": "string"
    },
    "available_only": {
      "default": false,
      "title": "Available Only",
      "type": "boolean"
    }
  },
  "title": "list_metricsArguments"
}

get_metric_metadata

Describe one metric before you use it: parity, category, minimum interval and any caveat attached to it. CALL THIS BEFORE get_metric_timeseries if the metric is unfamiliar. The minimum interval tells you the finest resolution that is real rather than interpolated, and the caveat is where an approximate metric admits what it approximates.

Parameters1

metric

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "metric": {
      "title": "Metric",
      "type": "string"
    }
  },
  "required": [
    "metric"
  ],
  "title": "get_metric_metadataArguments"
}

get_metric_timeseries

Timeseries for any available metric on any asset. Accepts Santiment-style relative dates ("utc_now-7d") as well as ISO timestamps. If the metric is not available this returns an error naming it rather than an empty series — an empty result here always means "no data in that range", never "we do not have this metric".

Parameters6

metric	string	required
asset	string	required
from_date	string	optional
to_date	string	optional
interval	string	optional
aggregation	string	optional

Raw schema

{
  "type": "object",
  "properties": {
    "metric": {
      "title": "Metric",
      "type": "string"
    },
    "asset": {
      "title": "Asset",
      "type": "string"
    },
    "from_date": {
      "default": "utc_now-30d",
      "title": "From Date",
      "type": "string"
    },
    "to_date": {
      "default": "utc_now",
      "title": "To Date",
      "type": "string"
    },
    "interval": {
      "default": "1d",
      "title": "Interval",
      "type": "string"
    },
    "aggregation": {
      "default": "LAST",
      "title": "Aggregation",
      "type": "string"
    }
  },
  "required": [
    "metric",
    "asset"
  ],
  "title": "get_metric_timeseriesArguments"
}

get_entity_flows

Whale flow per labelled entity over the window. Inflow to an exchange is distribution pressure; outflow is accumulation.

Parameters2

hours	integer	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "hours": {
      "default": 168,
      "title": "Hours",
      "type": "integer"
    },
    "limit": {
      "default": 20,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_entity_flowsArguments"
}

get_metric_timeseries_multi

Timeseries for ONE metric across MANY assets in a single call. `assets` is comma-separated (e.g. "BTC,ETH,SOL"). Prefer this over looping get_metric_timeseries — it is one round trip instead of N, and the values are guaranteed to come from the same read.

Parameters5

metric	string	required
assets	string	required
from_date	string	optional
to_date	string	optional
interval	string	optional

Raw schema

{
  "type": "object",
  "properties": {
    "metric": {
      "title": "Metric",
      "type": "string"
    },
    "assets": {
      "title": "Assets",
      "type": "string"
    },
    "from_date": {
      "default": "utc_now-30d",
      "title": "From Date",
      "type": "string"
    },
    "to_date": {
      "default": "utc_now",
      "title": "To Date",
      "type": "string"
    },
    "interval": {
      "default": "1d",
      "title": "Interval",
      "type": "string"
    }
  },
  "required": [
    "metric",
    "assets"
  ],
  "title": "get_metric_timeseries_multiArguments"
}

get_metrics_batch

Latest value of MANY metrics for ONE asset in a single call. `metrics` is comma-separated. The mirror of get_metric_timeseries_multi.

Parameters2

metrics	string	required
asset	string	required

Raw schema

{
  "type": "object",
  "properties": {
    "metrics": {
      "title": "Metrics",
      "type": "string"
    },
    "asset": {
      "title": "Asset",
      "type": "string"
    }
  },
  "required": [
    "metrics",
    "asset"
  ],
  "title": "get_metrics_batchArguments"
}

screen_assets

Filter the whole asset universe server-side and return the matches. `filter` is comma-separated `field:op:value` terms, e.g. "market_cap:gt:1000000000,funding_rate:lt:0" — assets over $1B whose funding rate is negative. Ops: gt, gte, lt, lte, eq, ne. Call screener_fields() first to see what fields exist and their ranges; guessing a field name gets the whole query rejected.

Parameters3

filter	string	optional
sort	string	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "filter": {
      "default": "",
      "title": "Filter",
      "type": "string"
    },
    "sort": {
      "default": "",
      "title": "Sort",
      "type": "string"
    },
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "screen_assetsArguments"
}

screener_fields

Every field the screener accepts, with type and description. Call this before building a filter rather than guessing field names.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "screener_fieldsArguments"
}

get_ohlcv

Daily candles (open/high/low/close/volume) for an asset, with the venue they came from. Coverage is bounded by which assets have a USDT pair on Binance or Bybit — an asset absent here has no candle source we collect, which is not the same as having no price.

Parameters2

symbol	string	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "default": "",
      "title": "Symbol",
      "type": "string"
    },
    "limit": {
      "default": 100,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_ohlcvArguments"
}

get_trending_assets

Assets trending now by social activity, with the hype score that separates a real move from a burst of noise.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_trending_assetsArguments"
}

get_entities

Named entities extracted from social documents about an asset — people, organisations, products and other tickers mentioned alongside it. Use it to find what a narrative is actually about.

Parameters1

asset

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "asset": {
      "title": "Asset",
      "type": "string"
    }
  },
  "required": [
    "asset"
  ],
  "title": "get_entitiesArguments"
}

get_sentiment_trends

Sentiment over time for an asset, with sample size and confidence interval. IMPORTANT: sentiment measured on few documents is unreliable — our own bootstrap put the direction wrong 35.6% of the time at n=1 and 9.5% at n=20. Read n before quoting a direction.

Parameters1

asset

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "asset": {
      "title": "Asset",
      "type": "string"
    }
  },
  "required": [
    "asset"
  ],
  "title": "get_sentiment_trendsArguments"
}

get_bot_detections

Accounts flagged as automated or coordinated, with the behavioural evidence. Volume from these should not be read as organic attention. READ `coverage` AND `note` BEFORE YOU READ THE LIST. No scorer is currently running, so `bot_probability` is NULL for every account and this list comes back EMPTY. An empty list here says nothing whatsoever about how clean the corpus is — it means nobody has been scored yet, and the response says so explicitly in `note`. `coverage.accounts_scored` vs `accounts_total` is the honest number: while the first is 0, treat this tool as reporting our coverage, not the market's cleanliness.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_bot_detectionsArguments"
}

get_coordinated_campaigns

Detected coordinated posting campaigns — the same message pushed by multiple accounts. Matching is exact-text, so this catches copypasta and misses the same campaign reworded.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_coordinated_campaignsArguments"
}

get_creator_rankings

Rank social creators across the whole corpus by measured influence. Corpus-wide, unlike get_influencers which is scoped to one asset. Influence is computed from engagement our collectors actually observed, so a creator we do not ingest is absent rather than ranked low — an absence here is a coverage fact, not a judgement.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 25,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_creator_rankingsArguments"
}

get_asset_revisions

Metrics for this asset that CHANGED after they were first published, with the old value, the new one and why. An agent that quoted an earlier number can find out here that it moved.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_asset_revisionsArguments"
}

get_trending_stories

Current narratives, each an LLM summary of a CLUSTER of posts rather than a single document. Read author_count before quoting one: a high post_count with a low author_count is one person repeating themselves, not a narrative. These are machine summaries, not edited articles.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 20,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_trending_storiesArguments"
}

get_narrative_clusters

The raw clusters behind the stories, without the prose — for a model doing its own summarisation. Every cluster has >= 2 distinct authors; near-identical posts from one account are copypasta and excluded.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 25,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_narrative_clustersArguments"
}

list_research_reports

List the assets that currently have a generated research report available. Returns the index, not the reports — call get_research_report with a symbol for the body. An asset missing from this list has not been written up; that is a statement about our coverage, not about the asset.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "list_research_reportsArguments"
}

get_research_report

One asset's generated report. The `unavailable` field lists metrics the report could NOT use — read it, because a report that silently omits funding rate reads as a report about an asset with unremarkable funding.

Parameters1

symbol

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_research_reportArguments"
}

resolve_ticker

Does a bare ticker actually mean the crypto asset? verdict 'crypto' means mentions are about the asset; 'equity'/'other' means the bare word is dominated by something else (TIA is Spanish 'tia'; GRT collides with 'graph'). A 404 is NOT a clean bill of health — it means nobody has adjudicated that ticker yet.

Parameters1

ticker

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "ticker": {
      "title": "Ticker",
      "type": "string"
    }
  },
  "required": [
    "ticker"
  ],
  "title": "resolve_tickerArguments"
}

get_social_coverage

How many assets actually clear the document floor that makes each social metric computable. Call this BEFORE quoting sentiment for an asset. A large corpus total does not mean sentiment works everywhere: attention is a power law and the documents pile onto BTC, so most assets stay uncomputable.

Parameters1

band

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "band": {
      "default": 300,
      "title": "Band",
      "type": "integer"
    }
  },
  "title": "get_social_coverageArguments"
}

list_nl_screens

Previously compiled natural-language screens: the English somebody wrote and the filter it compiled to, INCLUDING refusals. Useful as worked examples of the screener's filter grammar before you write one.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 25,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "list_nl_screensArguments"
}

get_agent_findings

What this platform's own monitoring agents are currently complaining about — dead collectors, stale data streams, coverage drops. Worth checking before relying on a number: a stream flagged stale here is still being served, it is just old.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_agent_findingsArguments"
}

run_sql

Run a read-only SELECT against the platform's data. One statement, 15s timeout, 10,000-row cap; truncation is always reported. Call get_sql_schema first for the queryable relations.

Parameters1

query

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "title": "Query",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "title": "run_sqlArguments"
}

get_sql_schema

List every relation and column queryable via run_sql, plus the rules and what is deliberately not exposed.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_sql_schemaArguments"
}

get_hodl_waves

Bitcoin supply split by coin age over time — which cohorts are holding and which are moving.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 260,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_hodl_wavesArguments"
}

get_exchange_netflow

Per-token flow onto and off exchanges. Inflow is distribution pressure, outflow is accumulation. net_usd where the token can be priced.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 40,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_exchange_netflowArguments"
}

get_constellation

Asset co-mention graph — which assets are discussed together, with what was filtered out.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 150,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_constellationArguments"
}

get_social_posts

Collected social posts, optionally filtered by platform (bluesky, farcaster, reddit, 4chan, bitcointalk, rss).

Parameters2

platform	string	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "platform": {
      "default": "",
      "title": "Platform",
      "type": "string"
    },
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_social_postsArguments"
}

get_similar_posts

Posts nearest a given post in embedding space. Read the returned BAND, never the raw cosine — 43% of this corpus sits at 0.80-0.90 by default.

Parameters1

post_id

integer

required

Raw schema

{
  "type": "object",
  "properties": {
    "post_id": {
      "title": "Post Id",
      "type": "integer"
    }
  },
  "required": [
    "post_id"
  ],
  "title": "get_similar_postsArguments"
}

get_asset_social_signal

Per-asset social signal over time. The `available` field states plainly whether we hold it.

Parameters2

symbol	string	required
days	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "title": "Symbol",
      "type": "string"
    },
    "days": {
      "default": 30,
      "title": "Days",
      "type": "integer"
    }
  },
  "required": [
    "symbol"
  ],
  "title": "get_asset_social_signalArguments"
}

get_metric_revisions

Restatements of published numbers — what changed, over which period, and why. A correction is not a market move.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_metric_revisionsArguments"
}

get_emerging_dex_pairs

Newly created DEX pairs above a USD liquidity floor. THE FLOOR IS RETURNED IN THE RESPONSE, and it is load-bearing: this is the long tail where most pairs are rugs or noise, and the floor is the only thing separating a signal from a list of scams. Raising it shrinks the result set and raises its quality.

Parameters1

min_liquidity_usd

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "min_liquidity_usd": {
      "default": 50000,
      "title": "Min Liquidity Usd",
      "type": "integer"
    }
  },
  "title": "get_emerging_dex_pairsArguments"
}

get_exchange_listings

Recent exchange listing announcements — an asset being added to a venue. A listing is an ATTENTION event, not a fundamental one: it changes who can buy, not what the project is worth. Use it to explain a volume or social spike, not as a valuation input.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 40,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_exchange_listingsArguments"
}

get_address_coverage

What we index per chain for watched addresses: what we see, what we miss, and what an empty feed actually means.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "get_address_coverageArguments"
}

get_address_events

Events on watched addresses. Check get_address_coverage before reading an empty result as silence — below the observed USD floor, movement is invisible to us.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_address_eventsArguments"
}

get_story_posts

The individual posts a story was assembled from — the receipts. Call this whenever a story matters enough to check. A generated story is a summary over these posts; this is how you verify it says what the sources say rather than taking the summary on trust.

Parameters1

story_id

integer

required

Raw schema

{
  "type": "object",
  "properties": {
    "story_id": {
      "title": "Story Id",
      "type": "integer"
    }
  },
  "required": [
    "story_id"
  ],
  "title": "get_story_postsArguments"
}

get_narrative_rotation

Which crypto narrative is GAINING or LOSING attention share, with a liquidity confirmation leg. Compares the last `days` against the equally long window immediately before. Covers the 2026 narrative set — RWA, tokenized equities and treasuries, stablecoins, DePIN, perp DEXs, prediction markets, AI agents — as well as DeFi, NFT, Gaming and the L1/L2 split. READ `vocabulary_stale` BEFORE QUOTING ANY DELTA. When true, the corpus spans two keyword vocabularies and a narrative whose keywords were just added will appear to be rising purely because only recent posts were ever tested against them. That is a rotation signal manufactured by a deploy, not by the market. A narrative with `unconfirmed: true` has measured attention and NO liquidity confirmation — the confirmation is absent, not zero, and the row states why.

Parameters2

days	integer	optional
include_legacy	boolean	optional

Raw schema

{
  "type": "object",
  "properties": {
    "days": {
      "default": 7,
      "title": "Days",
      "type": "integer"
    },
    "include_legacy": {
      "default": false,
      "title": "Include Legacy",
      "type": "boolean"
    }
  },
  "title": "get_narrative_rotationArguments"
}

get_ai_substance

Does an AI-sector project actually ship code, against how much attention it gets. Four states, and the fourth is not a verdict: ships public repo with development activity in 30 days silent public repo, no activity in 30 days no_public_repo no repository is published for this asset unmapped WE have not checked. This is a gap in OUR coverage and must NEVER be reported as the project failing to ship. RENDER sat in this state with 447 posts while publishing code the whole time. `dev_events_30d` is null rather than 0 for the last two states: there is no repository to have produced a zero. `attention_without_substance` is only ever set where the state was actually measured.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 40,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_ai_substanceArguments"
}

get_wrapper_basis

Cross-wrapper spread for tokenized equities: one real company, every issuer that tokenizes it, and how far apart they trade. SpaceX trades under five wrappers and they do not agree. Spreads run roughly 0.1-0.9 percent between programs referencing the same share. This is a CROSS-WRAPPER comparison, deliberately not a comparison against the underlying stock — that needs a licensed equity feed and no free commercially-usable one exists. A spread is NOT free money. Each issuer carries its own credit, redemption terms and transfer restrictions, and the cheapest wrapper is often cheapest for a reason. Do not present it as an arbitrage.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 40,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_wrapper_basisArguments"
}

get_disclosures

Public-record documents about issuers we track: SEC filings and federal court dockets. Exists because both RWA failures of 2026 were disclosed in public text before the price moved -- RealT's tax delinquency sat in court filings for a year, Goldfinch's borrower defaults were in governance forums before the vote. TWO THINGS YOU MUST NOT MISREPORT: A null `severity` means the document was FOUND and NOT ASSESSED. It is unjudged, not benign, and must never be summarised as "nothing concerning". `match_confidence: name_unverified` means the document was matched on a NAME and may concern a different company entirely -- searching for 'RealT' returns 'Broadway White Realty'. Do not attribute an unverified filing to an issuer without checking it. An empty result means nothing has been found, which for a subject never searched is not a statement about them at all.

Parameters2

symbol	string	optional
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "symbol": {
      "default": "",
      "title": "Symbol",
      "type": "string"
    },
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "get_disclosuresArguments"
}

README.md14 KBView on GitHub

Onvexia — crypto research and market intelligence

Onvexia: Crypto Research & Market Intelligence, for AI

Is the hype real? Is the token overvalued? Who is accumulating? Was that influencer ever right?

Crypto fundamental analysis, sentiment, whale tracking and on-chain analytics, for traders, investors and AI agents 66 read-only tools · 2,300+ coins · works in Claude, ChatGPT and Cursor · no install · free API key

Connect it in one line

bash

claude mcp add --transport http onvexia https://onvexia.com/mcp \
  --header "X-API-Key: YOUR_ONVEXIA_KEY"

The server is hosted: nothing to clone and nothing to run. Get a free key at onvexia.com/signup (then Account, API keys). During the launch offer every tool is free on an account. Without a key, one tool (get_data_coverage) answers, and every other tool replies with how to add a key or pay per call.

ChatGPT, Cursor, Windsurf, Cline, and any other MCP client

Any client that speaks Streamable HTTP:

json

{
  "mcpServers": {
    "onvexia": {
      "type": "http",
      "url": "https://onvexia.com/mcp",
      "headers": { "X-API-Key": "YOUR_ONVEXIA_KEY" }
    }
  }
}

Or install across clients with Smithery:

bash

npx -y @smithery/cli install farazgr8007/onvexia

The problem this solves

If you trade or invest: by the time a coin is trending on your timeline, you cannot tell whether 40,000 mentions came from 40,000 people or 400 bots, whether the wallets buying are accumulating or are exchange hot wallets about to sell, or whether the account that called it has ever been right before. That information exists on-chain and in public posts. It is just not in one place at the moment you need it.

If you build with AI: an LLM asked about a token answers from training data months old, and — worse — cannot tell you when it does not know. A quiet market and a hole in its knowledge look identical in the answer you get back.

Onvexia gives both live measurements, and makes the answer say which is which.

What you can ask it

Once connected, these are ordinary questions in plain English:

Trading and research

"Is the hype around $TOKEN real, or is it bots?"
"Which wallets moved the most ETH onto exchanges this week?"
"Find coins under $100M market cap with rising social volume and a flat price."
"What narratives is money rotating into right now?"
"Show me new DEX pairs above $250k liquidity from the last 48 hours."

Fundamental analysis and due diligence

"Give me the full fundamentals on $TOKEN — FDV, revenue, TVL, treasury, unlocks."
"Is $TOKEN overvalued? Show market cap versus fully diluted valuation."
"How much revenue does this protocol actually earn, and does it accrue to the token?"
"How big is their DAO treasury, and how much of it is their own token?"
"Has this protocol ever been exploited?"
"What unlocks are coming for $TOKEN, and how concentrated are the holders?"
"Does this AI-sector project actually ship code, against how much attention it gets?"
"Are there SEC filings or court dockets naming this issuer?"

Accountability

"Did this influencer call the move before it happened, or after?"
"Rank the top crypto influencers by measured accuracy, not follower count."
"Show me every call @rektcapital made on SOL and whether it came true at 7 days."

Sanity checks

"What data do you actually have on this token?"

What it does

Onvexia is a crypto research and market intelligence platform for traders, investors, analysts and AI agents. Find out whether a token's hype is real or manufactured, which wallets are quietly accumulating or dumping, which altcoins have social momentum building before the price moves, and whether the influencer calling it has ever actually been right.

Use it through any AI assistant (Claude, ChatGPT, Cursor) by connecting one URL, with nothing to install. Add a free Onvexia API key, or pay per call in USDC over x402 with no account.

Use Onvexia to:

▪ Check if crypto hype is real or bot-driven before you buy ▪ Spot pump-and-dump and coordinated shill campaigns while they are forming ▪ Track whale wallets, smart money and exchange inflows/outflows across 80,000+ labelled addresses ▪ Find trending altcoins early with social volume and sentiment from X/Twitter, Reddit, Farcaster and Bluesky ▪ Rank 2,300+ coins by Galaxy Score and AltRank, with the components behind every score ▪ Check whether a crypto influencer's calls came true — every call as a receipt linked to the original post, with the hit rate at 24h, 7d and 30d measured against holding BTC ▪ Run full crypto fundamental analysis on any token: market cap vs fully diluted valuation (FDV), tokenomics and supply schedule, protocol revenue, TVL, DAO treasury size and composition, exploit history, governance proposals, derivatives open interest, valuation ratios and a graded scorecard ▪ Do token due diligence before you buy: unlock schedules, holder concentration, competitive rank against peers ▪ Screen 2,300+ cryptocurrencies on social, on-chain and fundamental filters ▪ Find emerging DEX pairs and new exchange listings before they trend ▪ Query the entire dataset directly with read-only SQL

The MCP is public and read-only: connect with no install. To use the 66 tools, add a free Onvexia API key (sign up at onvexia.com; every tool is free on an account during the launch offer), or pay per call in USDC over x402 with no account at all.

Every response separates what was measured from what is not held. Sections carry a state, coverage counts ship alongside the results, and a null label means no label is held — never that a wallet or an influencer is safe. An empty list is never by itself evidence that nothing happened.

Galaxy Score and AltRank measure attention and social-market health. They are not valuation, price targets, or investment advice. Nothing here is a recommendation to buy or sell.

MCP endpoint: https://onvexia.com/mcp API documentation: https://onvexia.com/openapi.json Agent contract: https://onvexia.com/llms.txt Pricing manifest: https://onvexia.com/.well-known/x402

Try it right now, without installing anything

console

$ curl -s -X POST https://onvexia.com/mcp \
    -H 'Content-Type: application/json' \
    -H 'Accept: application/json, text/event-stream' \
    -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
         "params":{"name":"get_data_coverage","arguments":{}}}'

{
  "assets": 2308,
  "assets_priced": 2308,
  "labelled_addresses": 80062,
  "whale_transactions": 118214,
  "social_posts": 663639,
  "chains_with_onchain_flows": 7
}

A real response from the live endpoint. The numbers move: the corpus grew by 1,376 posts between two runs an hour apart.

get_data_coverage is the tool to call first. It reports what is actually held right now, so an agent can tell a quiet market from a hole in the data before it reasons about either. There is also a pure-stdlib example in examples/quickstart.py.

All 66 tools

Group	Tools
Assets	`get_asset` · `search_assets` · `get_asset_platforms` · `get_asset_revisions` · `resolve_ticker` · `get_entities`
Coverage	`get_data_coverage` · `get_chain_coverage` · `get_social_coverage` · `get_address_coverage`
Scores	`get_asset_scores` · `get_top_galaxy_scores` · `get_top_altranks` · `get_creator_rankings`
Social	`get_social_dominance` · `get_topic_rank` · `get_influencers` · `get_trending_assets` · `get_social_posts` · `get_similar_posts` · `get_asset_social_signal`
Sentiment	`analyze_sentiment` · `get_aspect_sentiment` · `get_sentiment_trends`
Signal integrity	`get_signal_integrity` · `get_bot_detections` · `get_coordinated_campaigns` · `get_ai_substance`
Influencer ledger	`get_influencer_ledger` (`view`: accountability · accuracy · calls) · `get_influencer_leaderboard` (`rank_by`: accountability · accuracy)
Fundamentals	`get_asset_fundamentals` · `get_asset_technicals` · `get_disclosures`
On-chain	`get_whale_transactions` · `get_exchange_flows` · `get_exchange_netflow` · `get_entity_flows` · `lookup_address` · `list_labelled_addresses` · `get_address_events` · `get_hodl_waves` · `get_constellation`
Market	`get_ohlcv` · `get_emerging_dex_pairs` · `get_exchange_listings` · `get_wrapper_basis`
Analytics	`get_correlation` · `get_leading_indicators`
Metrics	`list_metrics` · `get_metric_metadata` · `get_metric_timeseries` · `get_metric_timeseries_multi` · `get_metrics_batch` · `get_metric_revisions`
Screener	`screen_assets` · `screener_fields` · `list_nl_screens`
Narrative	`get_trending_stories` · `get_story_posts` · `get_narrative_clusters` · `get_narrative_rotation`
Research	`list_research_reports` · `get_research_report`
SQL	`run_sql` · `get_sql_schema`
Agents	`get_agent_findings`

Every tool carries a title and readOnlyHint, and no description is shorter than 80 characters. That is enforced, not maintained: the server refuses to build if a tool is missing from TOOL_TITLES, and tests/test_tool_metadata.py asserts the rest by reading the surface back through list_tools() — the same call your client makes. Nothing here writes, and no tool can move funds.

A free API key, or pay per call with no account

With a free Onvexia API key, sent as the X-API-Key header, every tool is free during the launch offer. With no account at all, metered endpoints can be paid per call with x402 (USDC on Base, via Coinbase's CDP facilitator), so an autonomous agent can use paid data without a human ever creating an account:

bash

export X402_CLIENT_PRIVATE_KEY=0x...   # a BURNER wallet, never a treasury key

$0.001 to $0.01 per call, published per endpoint in /.well-known/x402 and as x-payment-info on every priced operation in /openapi.json.

Three properties worth knowing before pointing it at mainnet:

It pays at most once per request. A retry loop around a payment endpoint is a wallet-drainer; a second 402 is raised, not paid again.
It refuses to overpay. The challenge is written by the server, so max_payment_atomic caps what one call can sign away.
You are never charged for a failure. A response that is not 2xx is never settled — the signed authorisation simply expires.

Honest data, enforced

This is the part most data APIs get wrong, and it matters more for an agent than for a human — a human notices an empty chart, an LLM confidently summarises it.

Sections carry a state: measured, not_held, or failed.
Coverage counts ship with the results, not on a status page.
A null label means we hold no label. It never means the wallet is clean.
"We never looked" and "we looked and found nothing" are different values.
Endpoints that cannot return data say so in the response — and are deliberately excluded from the paid catalogue rather than sold.

Run it yourself

You do not need to; the hosted endpoint is the supported path. This repository is the same server that runs it.

bash

pip install -r requirements.txt

# stdio, for a local client
CRYPTO_INTEL_API_URL=https://onvexia.com python3 server.py

# Streamable HTTP, the way the hosted endpoint runs
CRYPTO_INTEL_API_URL=https://onvexia.com MCP_HTTP_PORT=8091 python3 http_server.py

http_server.py refuses to start if CRYPTO_INTEL_API_KEY or X402_CLIENT_PRIVATE_KEY is set — one hosted instance serves every caller, so a shared key would meter everyone against one account and a shared wallet would pay strangers' payment challenges. Credentials are per request. See SECURITY.md.

Tests

bash

pip install -r requirements-dev.txt
pytest -q          # 50 tests, no network

Keywords

For traders and investors: crypto research tool · altcoin research · crypto market analysis · trending altcoins · crypto whale alerts · whale wallet tracker · smart money tracking · exchange inflow outflow · pump and dump detection · is this crypto a scam · crypto due diligence · token unlock schedule · holder concentration · crypto screener · social volume · crypto sentiment analysis · Galaxy Score · AltRank · crypto influencer accuracy · find early crypto gems · DEX pair scanner · new exchange listings

Fundamental analysis: crypto fundamental analysis · fundamental analysis tool for crypto · token fundamentals · tokenomics analysis · fully diluted valuation · FDV vs market cap · market cap to FDV ratio · token valuation ratios · protocol revenue · crypto revenue analysis · TVL tracker · DAO treasury tracker · treasury composition · token supply schedule · circulating vs total supply · crypto exploit history · protocol security audit · governance proposal tracker · derivatives open interest · crypto scorecard · token rating · crypto peer comparison · is this token overvalued

For developers and AI agents: crypto MCP server · Model Context Protocol crypto · crypto API · on-chain analytics API · crypto market intelligence API · bot detection crypto Twitter · Claude MCP crypto · ChatGPT crypto data · Cursor MCP · AI agent crypto tools · x402 · pay per call API · USDC on Base · free crypto API

License

MIT — see LICENSE.

Install

Tools (66)

get_assetget_asset_scoresget_asset_fundamentalsget_asset_technicalsget_top_galaxy_scoresget_top_altranksget_correlationget_leading_indicators

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://onvexia.com/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "onvexia": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://onvexia.com/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.