Subgraph Registry

Agent-friendly semantic classification of all subgraphs on The Graph Network.

Pre-computed index of 14,700+ subgraphs with domain classification, protocol type detection, schema fingerprinting, canonical entity mapping, and composite reliability scoring.

What's new in 0.8.0 — three agent-discovery upgrades:

• Semantic search via 384-dim embeddings (semantic_search_subgraphs)
• Schema evolution tracking with stability days surfaced on every result (get_schema_changes)
• OpenAPI 3.1 spec auto-generated for MCP tools + REST routes, served at /.well-known/openapi.json

The Problem

Agents querying The Graph need to discover and select the right subgraph before they can query data. Today this requires 3-4 tool calls (search, check volumes, fetch schema, infer structure) before any real work happens. This registry flips that: agents start with structured knowledge, not a blank slate.

What It Does

1. Crawls all active subgraphs from the Graph Network meta-subgraph
2. Fetches the GraphQL schema for every deployment
3. Extracts contract addresses from each manifest's dataSources and templates — agents can answer "which subgraph indexes contract 0x… on chain X?"
4. Generates a per-subgraph starter GraphQL query from the parsed schema (real top entity, real fields, sensible orderBy) — no more generic boilerplate that doesn't compile against most subgraphs
5. Classifies each subgraph by domain, protocol type, canonical entities, and schema family
6. Scores reliability using on-chain signals (query fees, volume, curation, stake)
7. Returns x402 + legacy query URLs — agents can pay $0.01 USDC on Base per query (no API key) or use a Studio key
8. Publishes as SQLite database + REST API + MCP server + per-subgraph JSON-LD at /.well-known/subgraph/{id}.jsonld for ecosystem crawlers
9. Generates visual dashboards and bot-readable category files (auto-updated with each sync)

Querying with x402 (no API key)

Every result includes query_url_x402 alongside the legacy query_url. The Graph's public x402 gateway (live since 2026-05-08) accepts $0.01 USDC on Base per query with zero signup.

// An x402-native agent — discovery to data in two calls
const { recommendations } = await mcp.call("recommend_subgraph", {
  goal: "find DEX trades on Arbitrum",
});
const top = recommendations[0];

// POST your GraphQL query. The first call returns HTTP 402 with a
// base64 `payment-required` header; the x402 client signs the
// EIP-3009 USDC transfer on Base and retries automatically.
const data = await x402Fetch(top.query_url_x402, {
  method: "POST",
  body: JSON.stringify({ query: "{ swaps(first: 5) { id amountUSD } }" }),
});

Pricing manifest returned per subgraph:

{
  "amount_usd": 0.01,
  "asset": "USDC",
  "asset_contract": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
  "chain": "base",
  "network": "eip155:8453",
  "pay_to": "0x79DC34E41B2b591078d3dE222C43EcaaBD52FcCB",
  "scheme": "exact",
  "asset_transfer_method": "eip3009"
}

Client libraries: @graphprotocol/client-x402, x402-fetch, or any generic x402 wrapper.

Registry at a Glance

Charts auto-generated from registry.db on each sync. See python/generate_docs.py.

Browse by Category

Domains

Explore subgraphs by use case — each file lists the top 25 subgraphs ranked by reliability score.

Full index: docs/DOMAINS.md

Networks

Explore subgraphs by blockchain — each file lists the top 25 subgraphs on that chain.

Full index: docs/NETWORKS.md

Protocol Types

Reliability Score

Each subgraph gets a composite reliability score (0-1) based on four on-chain signals:

All values are log-scaled and capped at 1.0. A 0.5 penalty is applied if the subgraph has been denied/deprecated.

Score tiers: High (0.7+) = strong signal, real usage | Medium (0.3-0.7) = functional, some activity | Low (<0.3) = minimal signal or test deployment

MCP Server

The registry is available as an MCP server with dual transport — stdio for local clients and SSE/HTTP for remote agents.

The shipped server is the Node implementation in src/index.js; that's what npx subgraph-registry-mcp runs and what's published to npm. A Python equivalent in python/mcp_server.py is kept for local development against the same SQLite database — bug fixes and new tools should land in the Node version first.

6 tools:

• search_subgraphs — filter by domain, network, protocol type, entity, or keyword
• recommend_subgraph — natural language goal to best subgraphs (includes schema_stable_days)
• get_subgraph_detail — full classification for a specific subgraph (includes schema_changed_at)
• list_registry_stats — registry overview (domains, networks, counts)
• semantic_search_subgraphs — vector-similarity search over precomputed embeddings (sentence-transformers/all-MiniLM-L6-v2, 384-dim). Use for fuzzy/paraphrased goals where literal keyword match would miss.
• get_schema_changes — chronological schema-fingerprint history for a subgraph (one row per detected change). Helps agents prefer mature subgraphs whose data contract has been stable.

Install

# Claude Code
claude mcp add subgraph-registry -- npx subgraph-registry-mcp

# Claude Desktop
{
  "mcpServers": {
    "subgraph-registry": {
      "command": "npx",
      "args": ["subgraph-registry-mcp"]
    }
  }
}

# Remote agents (SSE)
npx subgraph-registry-mcp --http-only
# Then connect to http://localhost:3848/sse

The server auto-downloads the pre-built registry (8MB SQLite) from GitHub on first run.

Well-Known JSON-LD Manifest

Stable, machine-readable per-subgraph manifest that other crawlers and agent frameworks can index without going through MCP. Served by the Node MCP HTTP transport:

GET /.well-known/subgraph/{id}.jsonld     Full per-subgraph manifest (JSON-LD)
GET /subgraphs/{id}.jsonld                 Alias (same payload)
GET /.well-known/subgraph-index.jsonld     Discovery list — top 100 by reliability with @id links

Each manifest includes classification, parsed entities, contract addresses (from the indexed dataSources), endpoints (x402 + API-key), a per-subgraph starter query generated from the actual schema, pricing, and metadata. The @context + @type make the shape auto-discoverable.

# Start the HTTP transport
npx subgraph-registry-mcp --http-only

# Fetch the manifest for Uniswap V3 Mainnet
curl http://localhost:3848/.well-known/subgraph/5zvR82QoaXYFyDEKLZ9t6v9adgnptxYpKpSbxtgVENFV.jsonld

Semantic Search

Every subgraph has a precomputed 384-dim embedding from sentence-transformers/all-MiniLM-L6-v2, built from its display name, description, canonical entities, top schema entity names, and protocol metadata. At MCP-tool-call time the Node server embeds the query string with the same model (via @xenova/transformers, quantized ONNX bundled in the npm package — no first-call download) and ranks rows by cosine similarity.

const { subgraphs } = await mcp.call("semantic_search_subgraphs", {
  query: "lending positions near liquidation on a Layer 2",
  limit: 5,
});
// subgraphs[i].semantic_score is cosine similarity in [0, 1]; >0.5 ~= strong match.

Use it when:

• The goal is paraphrased or use-case-shaped (search_subgraphs is keyword-only).
• You're exploring "what data exists for X?" rather than fetching a specific protocol's subgraph.

Same model is shared between Python crawl-time (fastembed) and JS runtime (@xenova/transformers) — vectors are bitwise-comparable so cosine math gives consistent rankings across runtimes.

Embeddings add ~22 MB to registry.db (14k × 384 × 4 bytes); model bundle adds ~23 MB to the npm package.

Schema Evolution

Each crawl computes a schema_fingerprint (MD5 of sorted entity:field_count pairs) per subgraph. Whenever the fingerprint changes from the previous sync, an immutable row is written to schema_history. The table is append-only and survives full DB rebuilds.

const history = await mcp.call("get_schema_changes", {
  subgraph_id: "5zvR82QoaXYFyDEKLZ9t6v9adgnptxYpKpSbxtgVENFV",
});
// {
//   total_changes: 3,
//   stable_days: 47.2,
//   changed_within_24h: false,
//   changed_within_7d: false,
//   changes: [
//     { fingerprint: "abc123...", prev_fingerprint: "def456...", detected_at: 1717... },
//     ...
//   ]
// }

recommend_subgraph and get_subgraph_detail results now also include schema_changed_at (unix seconds of last detected change) and schema_stable_days so agents can prefer subgraphs whose data contract has been stable longer — useful when a query needs to keep working across the agent's planning horizon.

OpenAPI

The full API surface (MCP tools + REST routes) is published as OpenAPI 3.1:

• openapi.yaml — checked into the repo, single source of truth
• data/openapi.json — bundled with the npm tarball
• GET /.well-known/openapi.json — served by the HTTP transport for live discovery

The spec is regenerated on every release from the declarative TOOLS[] + REST_ROUTES[] exports in src/index.js via scripts/gen-openapi.js. CI fails any PR that touches src/index.js without regenerating the spec.

REST API

GET /summary                    Registry overview and stats
GET /domains                    Domain breakdown
GET /networks                   Network breakdown
GET /families                   Schema family groups (fork/clone detection)
GET /subgraphs                  Filter subgraphs
GET /subgraphs/{id}             Full detail for one subgraph (now includes contract_addresses and example_query)
GET /search?q=uniswap           Free-text search
GET /recommend?goal=...&chain=  Agent-optimized recommendation

# Start API server
cd python && python server.py

# Example: find DEX subgraphs on Arbitrum
curl "http://localhost:3847/recommend?goal=query+DEX+trades+on+Arbitrum&chain=arbitrum-one"

# Example: filter by entity type
curl "http://localhost:3847/subgraphs?entity=liquidity_pool&network=base&min_reliability=0.5"

Bot-Readable Category Files

The docs/ directory contains structured .md files with YAML frontmatter designed for AI agents and bots to consume:

docs/
├── DOMAINS.md           # Index of all domains with counts
├── NETWORKS.md          # Index of all networks with counts
├── charts/              # Auto-generated SVG visualizations
│   ├── domains.svg
│   ├── networks.svg
│   ├── protocol-types.svg
│   └── reliability.svg
├── domains/             # One file per domain
│   ├── defi.md          # Top 25 DeFi subgraphs by reliability
│   ├── nfts.md
│   ├── dao.md
│   └── ...
└── networks/            # One file per network
    ├── mainnet.md       # Top 25 Ethereum subgraphs by reliability
    ├── base.md
    ├── arbitrum-one.md
    └── ...

Each category file includes:

• YAML frontmatter (domain/network, count, percentage, last updated)
• Top 25 subgraphs ranked by reliability score
• MCP tool and REST API query examples

Architecture

Graph Network Subgraph (meta-subgraph, 140M queries/month)
    |
    v
crawler.py ---- async httpx, ID-based cursor pagination
    |
    v
classifier.py - rule-based domain/protocol classification + schema fingerprinting
    |
    v
registry.py --- builds SQLite + indices
    |
    ├── server.py ------ FastAPI REST API (:3847)
    ├── generate_docs.py SVG charts + category .md files
    └── scheduler.py --- weekly incremental sync

MCP Server (src/index.js, published to npm)
    ├── stdio   ←── Claude Desktop / Claude Code
    └── SSE     ←── OpenClaw / remote agents (:3848)

python/mcp_server.py — local-dev MCP server hitting the same SQLite DB

Quick Start (Local Build)

cd python
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

echo "GATEWAY_API_KEY=your-key-here" > .env

# Full crawl + classify (~11 min)
python registry.py

# Generate charts and category files
python generate_docs.py

# Start API server
python server.py

How It Stays Current

A GitHub Actions workflow runs every 3 days:

1. Incremental crawl (updatedAt_gte: lastSyncTimestamp)
2. Reclassify new/changed subgraphs
3. Regenerate SVG charts and category .md files
4. Commit and push updates

License

MIT