ai.mcpanalytics/analytics

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The statistical analyst in your AI chat — validated, citable, re-runnable analysis of your data.

ai.mcpanalytics/analytics MCP Server

The ai.mcpanalytics/analytics server provides an “statistical analyst in your AI chat” that performs validated, citable, re-runnable analysis. It accepts a CSV or can connect to a live source, generates custom analysis for the given question, and returns an interactive report that can be rerun on fresh data.

🛠️ Key Features

CSV input and live source connectivity (per description excerpt)
Validated methodology and citable results
Custom analysis tailored to provided data and question
Interactive report output
Queryable from Claude, Cursor, or any MCP client

🚀 Use Cases

CSV-based analysis with a specific question
Re-running analyses on refreshed data
Generating interactive reports for data analytics workflows

⚡ Developer Benefits

Integrates with Model Context Protocol (MCP) clients, including Claude and Cursor
Supports rerunnable analysis workflows
Enables access to analysis via an MCP client interface

⚠️ Limitations

Server description excerpt does not enumerate individual tools (toolCount is 17, but not listed)
Details of the exact live source types and configuration are not included in the provided excerpt

statisticsdata analysishypothesis testingreproducible researchanalytics

Topics

analyticsclaudecursordata-sciencemachine-learningmcpmodel-context-protocolstatisticsdata-analyticsmcp-serverai-agentsai-toolsanthropicbusiness-intelligencecsv-analysisinteractive-reportsllm-toolsno-codeshopify-analyticsstripe-analytics

Captured live from the server via tools/list.

account_link

Direct link to the right account page for anything not doable in chat — billing, browser upload, report management. Hand the user the link and guide them.

Parameters1

section

string

optional

Where to send the user

Raw schema

{
  "type": "object",
  "properties": {
    "section": {
      "type": "string",
      "enum": [
        "home",
        "billing",
        "upgrade",
        "create",
        "upload",
        "reports"
      ],
      "description": "Where to send the user"
    }
  },
  "required": []
}

about

Platform documentation and info — how it works, tiers, usage.

Parameters1

topic

string

required

Topic: platform, manual, connectors, or a docs section

Raw schema

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "description": "Topic: platform, manual, connectors, or a docs section"
    }
  },
  "required": [
    "topic"
  ]
}

agent_advisor

AI help desk — which analysis fits your question, interpreting results, fixing errors. Multi-turn.

Parameters1

message

string

required

Your question or request

Raw schema

{
  "type": "object",
  "properties": {
    "message": {
      "type": "string",
      "description": "Your question or request"
    }
  },
  "required": [
    "message"
  ]
}

datasets_upload

Get your data in. Pass `data` as an array of row objects to create the dataset immediately and get a dataset_ref ready for create_analysis; omit it to get an upload link for a file only the user can reach. Add replace_ref (uuid://ID:KEY) with data to REFRESH an existing dataset in place — schedules and tools holding that reference read the new data on their next run.

Parameters3

data	array	optional	Rows as an array of flat objects — creates the dataset in one call
replace_ref	string	optional	uuid://ID:KEY of an existing dataset to overwrite in place with `data` (the push/refresh mode)
expires_in	integer	optional	Token expiration in seconds

Raw schema

{
  "type": "object",
  "properties": {
    "data": {
      "type": "array",
      "description": "Rows as an array of flat objects — creates the dataset in one call",
      "items": {
        "type": "object"
      }
    },
    "replace_ref": {
      "type": "string",
      "description": "uuid://ID:KEY of an existing dataset to overwrite in place with `data` (the push/refresh mode)"
    },
    "expires_in": {
      "type": "integer",
      "description": "Token expiration in seconds",
      "default": 300
    }
  }
}

datasets_list

List and search your uploaded datasets — fuzzy matching on name, description, and tags. Returns each dataset's uuid:// reference for use in create_analysis and run_analysis.

Parameters2

search	string	optional	Search by name, description, or tags
limit	integer	optional	Max results

Raw schema

{
  "type": "object",
  "properties": {
    "search": {
      "type": "string",
      "description": "Search by name, description, or tags"
    },
    "limit": {
      "type": "integer",
      "description": "Max results",
      "default": 20
    }
  }
}

discover_tools

Browse the analyses you can run — the ones you commissioned plus the platform Standard Library (prebuilt tools; each result tagged source:'own' or 'standard_library'). Plain-language match; no query lists everything, your own first. Nothing fits? Commission it with create_analysis.

Parameters1

query

string

optional

Plain-language search over your library + the Standard Library; omit to list everything

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Plain-language search over your library + the Standard Library; omit to list everything"
    }
  }
}

tools_schema

Get an analysis's parameter schema — ALWAYS call before run_analysis.

Parameters1

tool_name

string

required

Name of the analysis

Raw schema

{
  "type": "object",
  "properties": {
    "tool_name": {
      "type": "string",
      "description": "Name of the analysis"
    }
  },
  "required": [
    "tool_name"
  ]
}

run_analysis

Run an analysis on your data. Returns a shareable interactive report URL — validated statistics you can cite, re-run, and share.

Parameters2

tool_name	string	required	Name of the analysis to run
taskList	object	required	Execution inputs. Call tools_schema first for the analysis-specific fields.

Raw schema

{
  "type": "object",
  "properties": {
    "tool_name": {
      "type": "string",
      "description": "Name of the analysis to run"
    },
    "taskList": {
      "type": "object",
      "description": "Execution inputs. Call tools_schema first for the analysis-specific fields.",
      "properties": {
        "inputs": {
          "type": "object",
          "properties": {
            "dataset": {
              "type": "string",
              "description": "Dataset reference: uuid://UUID:KEY from datasets_upload/datasets_list"
            },
            "userContext": {
              "type": "object",
              "description": "Business context: objective (what you want to learn, required) and company"
            },
            "column_mapping": {
              "type": "object",
              "description": "Analysis-specific column mapping — see tools_schema"
            },
            "module_parameters": {
              "type": "object",
              "description": "Analysis-specific parameters — see tools_schema"
            }
          }
        }
      }
    }
  },
  "required": [
    "tool_name",
    "taskList"
  ]
}

create_analysis

Commission a NEW analysis built for your question. tier is REQUIRED — the user picks. Easiest: fuzzy_request (plain language) + dataset_ref + tier. Snapshot = instant automated report (~2-10 min). JSON = a fast computed answer, numbers + method, re-runnable tool you own (~5 min). Brief = the computed answer on a one-page report — chart, numbers, method (~7 min). Deck = commissioned deep analysis, a durable re-runnable module you own (30-45 min). Failed builds are never billed.

Parameters7

fuzzy_request	string	optional	Plain-language description of the analysis you want
dataset_ref	string	optional	Single-dataset URI: 'uuid://UUID:KEY'
datasets_refs	object	optional	Multi-dataset URIs keyed by role
tier	string	optional	snapshot = instant report (~2-10 min); json = fast computed answer (~5 min, default); brief = one-page report of the answer (~7 min); deck = commissioned re-runnable module (30-45 min)
specification	object	optional	Full 11-field spec (legacy path — prefer fuzzy_request)
column_mapping	object	optional	Optional semantic-to-real column map (hint only)
notes	string	optional	Optional context for the build — constraints, definitions, or preferences the analyst agents should honor

Raw schema

{
  "type": "object",
  "properties": {
    "fuzzy_request": {
      "type": "string",
      "description": "Plain-language description of the analysis you want"
    },
    "dataset_ref": {
      "type": "string",
      "description": "Single-dataset URI: 'uuid://UUID:KEY'"
    },
    "datasets_refs": {
      "type": "object",
      "description": "Multi-dataset URIs keyed by role"
    },
    "tier": {
      "type": "string",
      "enum": [
        "snapshot",
        "json",
        "brief",
        "deck"
      ],
      "description": "snapshot = instant report (~2-10 min); json = fast computed answer (~5 min, default); brief = one-page report of the answer (~7 min); deck = commissioned re-runnable module (30-45 min)"
    },
    "specification": {
      "type": "object",
      "description": "Full 11-field spec (legacy path — prefer fuzzy_request)"
    },
    "column_mapping": {
      "type": "object",
      "description": "Optional semantic-to-real column map (hint only)"
    },
    "notes": {
      "type": "string",
      "description": "Optional context for the build — constraints, definitions, or preferences the analyst agents should honor"
    }
  },
  "required": []
}

modify_analysis

Modify an EXISTING analysis into a new version — reword the question, swap the method, or add a variable. Pass tool_name + changes (plain language). Rebuilds on the analysis's own dataset by default; the original stays put. Returns pipeline tracking — follow with build_status.

Parameters4

tool_name	string	required	The analysis to modify (from discover_tools or your library)
changes	string	required	What to change, in plain language, e.g. 'also break it down by region' or 'use a random forest instead'
tier	string	optional	Optional — change the depth of the new version
dataset_ref	string	optional	Optional — rebuild against a different dataset ('uuid://UUID:KEY')

Raw schema

{
  "type": "object",
  "properties": {
    "tool_name": {
      "type": "string",
      "description": "The analysis to modify (from discover_tools or your library)"
    },
    "changes": {
      "type": "string",
      "description": "What to change, in plain language, e.g. 'also break it down by region' or 'use a random forest instead'"
    },
    "tier": {
      "type": "string",
      "enum": [
        "snapshot",
        "json",
        "brief",
        "deck"
      ],
      "description": "Optional — change the depth of the new version"
    },
    "dataset_ref": {
      "type": "string",
      "description": "Optional — rebuild against a different dataset ('uuid://UUID:KEY')"
    }
  },
  "required": [
    "tool_name",
    "changes"
  ]
}

build_status

Check a commissioned build in-chat: stage progress, queue position, rejection reason if the data didn't match the objective, honest ETA, report link when delivered.

Parameters2

pipeline_id	integer	optional	pipeline_id from create_analysis
track_token	string	optional	Token from the tracking URL

Raw schema

{
  "type": "object",
  "properties": {
    "pipeline_id": {
      "type": "integer",
      "description": "pipeline_id from create_analysis"
    },
    "track_token": {
      "type": "string",
      "description": "Token from the tracking URL"
    }
  },
  "required": []
}

ask_library

Ask a question across all your delivered analyses — synthesized answer with citations back to specific reports.

Parameters1

question

string

required

Plain-language question to answer from your report library

Raw schema

{
  "type": "object",
  "properties": {
    "question": {
      "type": "string",
      "description": "Plain-language question to answer from your report library"
    }
  },
  "required": [
    "question"
  ]
}

reports_list

Your report library — every analysis delivered, with status and links. Pass semantic_query to search report content in plain language.

Parameters2

semantic_query	string	optional	Natural-language search over your reports' content
limit	integer	optional	Max results

Raw schema

{
  "type": "object",
  "properties": {
    "semantic_query": {
      "type": "string",
      "description": "Natural-language search over your reports' content"
    },
    "limit": {
      "type": "integer",
      "description": "Max results",
      "default": 10
    }
  }
}

warehouse

Query your org's data warehouse free: browse the catalog (tables with column roles + computed metrics), semantically find data, plain-language ask, or named templates. Requires warehouse enablement (business plans).

Parameters4

action	string	required
question	string	optional
query	string	optional
params	object	optional

Raw schema

{
  "type": "object",
  "properties": {
    "action": {
      "type": "string",
      "enum": [
        "ask",
        "query",
        "queries",
        "catalog",
        "find"
      ]
    },
    "question": {
      "type": "string"
    },
    "query": {
      "type": "string"
    },
    "params": {
      "type": "object"
    }
  },
  "required": [
    "action"
  ]
}

schedules

Standing re-runs of analyses you own: action='create' (weekly/monthly against a re-runnable data reference — connector:// or an https:// link; report emailed after each run), 'list', or 'cancel'.

Parameters6

action	string	required	What to do
tool_name	string	optional	create: the analysis to schedule
dataset_ref	string	optional	create: re-runnable reference (connector:// or https://)
cadence	string	optional
column_mapping	object	optional
schedule_id	integer	optional	cancel: from action='list'

Raw schema

{
  "type": "object",
  "properties": {
    "action": {
      "type": "string",
      "enum": [
        "create",
        "list",
        "cancel"
      ],
      "description": "What to do"
    },
    "tool_name": {
      "type": "string",
      "description": "create: the analysis to schedule"
    },
    "dataset_ref": {
      "type": "string",
      "description": "create: re-runnable reference (connector:// or https://)"
    },
    "cadence": {
      "type": "string",
      "enum": [
        "weekly",
        "monthly"
      ]
    },
    "column_mapping": {
      "type": "object"
    },
    "schedule_id": {
      "type": "integer",
      "description": "cancel: from action='list'"
    }
  },
  "required": [
    "action"
  ]
}

reports_view

Get a shareable browser link for a report — viewable without authentication.

Parameters1

processing_id

string

required

Processing ID from run_analysis / reports_list

Raw schema

{
  "type": "object",
  "properties": {
    "processing_id": {
      "type": "string",
      "description": "Processing ID from run_analysis / reports_list"
    }
  },
  "required": [
    "processing_id"
  ]
}

report_cards

Browse a delivered report's individual cards (charts, tables, insights) inline in chat.

Parameters1

processing_id

string

required

The report's processing id, returned by run_analysis or build_status

Raw schema

{
  "type": "object",
  "properties": {
    "processing_id": {
      "type": "string",
      "description": "The report's processing id, returned by run_analysis or build_status"
    }
  },
  "required": [
    "processing_id"
  ]
}

README.md18 KBView on GitHub

MCP Analytics Suite

The statistical analyst in your AI chat. Bring a CSV (or connect a live source) and a question. A standing team of specialist agents builds a custom analysis specific to your data, validates the methodology, and ships back a citable, interactive report. The analysis is yours — it lives in your library, reruns on fresh data for a fraction of the creation cost, and is queryable from Claude, Cursor, or any MCP client. The work compounds.

This is the public listing and documentation repository. Issues, feature requests, and examples live here. The API server code is maintained separately.

Sample Reports → • Try Demo → • Pricing →

Try it before installing anything. The free tools run in the browser on a CSV you upload — no account, no key, no MCP client. Each one is a real analysis with the method written out: PCA, correlation, forecasting, RFM segmentation, regression (GLM).

Hire the team. Own the analysis. Rerun forever.

🚀 Quick Start • 🔄 How It Works • 🛠️ MCP Tools • 🛡️ Security • 📖 Documentation

Click to watch: Ask a question → upload data → get an interactive report with AI insights

Overview

You bring data and a question. A pipeline of specialist agents — spec drafter, builder, verifier, fixer, deployer — turns your question into a custom analysis for your data. The result is an interactive report: charts, AI-narrated insights, exportable PDF, embedded source code, citable. Every commissioned analysis joins your private library — query it from any MCP client, rerun on fresh data with one call, share with collaborators on your terms.

Cornerstone modules ship pre-built (t-tests, regression, churn, segmentation, forecasting, customer LTV, A/B testing, time series, survival analysis, and more) so you can see a finished report in under a minute and verify the team can build things that work. Custom analysis creation is the named revenue event — pay once to build the capability, own it, rerun for a fraction of the creation price. A build that fails is never billed.

Connect data however it lives: CSV upload, public URL, or live OAuth connectors for Google Analytics 4 and Google Search Console (more coming). Once a connector is linked, every rerun pulls fresh data automatically — no re-export step.

Choose Your Depth — Four Tiers

Every analysis runs through the same pipeline — you choose how far it goes:

Tier	What you get	Time
Snapshot	One chart and a verified insight — an instant read of your data, covered by your welcome credits	~2 min
JSON	One computed statistical answer — the numbers and the method — deployed as a tool you re-run on fresh data	~5 min
Brief	The computed answer, presented — chart, key figures, and method on a single shareable page	~7 min
Deck	The full study — a complete statistical report built to your brief and independently verified; a durable module you own and re-run forever	30–45 min

More rigor outranks more charts: going deeper buys real statistical methods — hypothesis tests, regression, diagnostics — not just more cards. You pay for depth, and only if the build succeeds. How the tiers work →

Why MCP Analytics

Citable — APA / MLA / Chicago / BibTeX in one click, ready for papers, decks, and regulatory filings
Sourceable — R source code embedded in every report; a skeptical reader can run it and get the same answer
Reproducible — fixed seeds, Docker isolation, named methods; same input → same output, forever
Yours — every commissioned module is private to your account; rerun on fresh data, query across your portfolio
MCP-native — query the library from Claude, Cursor, Windsurf, or any MCP client
Secure — OAuth2, encryption at rest, isolated container processing per analysis
Honest — when an analysis has issues, the team gives you a free re-run; the relationship is built on the report being right

Quick Start

1. Get an API Key

Sign up free at account.mcpanalytics.ai, go to account settings, and copy your API key (starts with mcp_). You get 9,000 welcome credits, no credit card required. That covers about seven full analyses at any depth, plus re-runs.

2. Connect

Three options — all connect to the same platform with the same tools.

Option A: npx Install (Recommended)

Works with Claude Desktop, Cursor, Windsurf, and any stdio MCP client. Requires Node.js 18+.

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

json

{
  "mcpServers": {
    "mcpanalytics": {
      "command": "npx",
      "args": ["-y", "@mcp-analytics/mcp-analytics"],
      "env": {
        "MCP_ANALYTICS_API_KEY": "mcp_your_key_here"
      }
    }
  }
}

Cursor / Windsurf — add to .cursor/mcp.json:

json

{
  "mcpServers": {
    "mcpanalytics": {
      "command": "npx",
      "args": ["-y", "@mcp-analytics/mcp-analytics"],
      "env": {
        "MCP_ANALYTICS_API_KEY": "mcp_your_key_here"
      }
    }
  }
}

Claude Code — run in your terminal:

bash

claude mcp add mcpanalytics -- npx -y @mcp-analytics/mcp-analytics
# Then set MCP_ANALYTICS_API_KEY in your environment

Option B: Direct API Key (No npm)

For MCP clients that support Streamable HTTP transport with custom headers:

json

{
  "mcpServers": {
    "mcpanalytics": {
      "url": "https://api.mcpanalytics.ai/mcp/api-key",
      "headers": {
        "X-API-Key": "mcp_your_key_here"
      }
    }
  }
}

Option C: OAuth2 (No API Key)

Zero-config — a browser opens for login on first connection:

json

{
  "mcpServers": {
    "mcpanalytics": {
      "url": "https://api.mcpanalytics.ai/auth0"
    }
  }
}

Browse Tools First (No Account Needed)

Explore the full tool catalog before signing up:

bash

# Static metadata (tool names, descriptions, all transport options)
curl https://api.mcpanalytics.ai/.well-known/mcp.json

# MCP protocol discovery (no auth — works with any MCP client)
curl -X POST https://api.mcpanalytics.ai/mcp/discover \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":1,"params":{}}'

3. Start Analyzing

Restart your MCP client. Ask:

"Upload sales.csv and find what drives revenue"
"What statistical test should I use for this survey data?"
"Forecast next quarter's sales from this time series"

How It Works

The MCP Analytics Workflow

Upload your data — datasets_upload securely processes your CSV (or reuse an existing dataset / connected source)
Commission the analysis — create_analysis takes your question in plain language, your dataset, and the tier you choose (snapshot, json, brief, or deck)
Watch it build — build_status reports progress, queue position, and the report link when done
Get the report — reports_view delivers the interactive report; report_cards displays individual cards inline
Rerun forever — run_analysis re-runs any analysis you own on fresh data for a fraction of the creation cost

code

User: "What drives our sales growth?"
MCP Analytics:
  → Scopes the right statistical method for your data's shape
  → Writes R in an isolated container — deterministic, fixed seeds
  → Runs it, then independently verifies numbers and narrative
  → Returns a citable, interactive report you own

MCP Tools

The platform provides a complete suite of MCP tools for end-to-end analytics:

Analysis

create_analysis - Commission a new analysis from a plain-language question, at the tier you choose
build_status - Track a build: stage progress, queue position, report link
run_analysis - Run an analysis you own (or one discovered via discover_tools) on fresh data
modify_analysis - Turn an existing analysis into a new version — reword the question, change the framing

Discovery

discover_tools - Browse what you can run: your commissioned analyses plus the prebuilt library
tools_schema - Get an analysis's parameter schema — always call this before run_analysis

Data Management

datasets_upload - Secure data upload with encryption
datasets_list - List and search your uploaded datasets

Connectors

connectors_list - List available data source connections
connectors_query - Pull live data from a connected source

Reporting & Insights

reports_view - Get a shareable browser link for a report
reports_list - Your report library — every analysis delivered, searchable in plain language
report_cards - Browse a delivered report's individual cards (charts, tables, insights)
ask_library - Ask one question across all your delivered analyses; get a synthesized answer with citations back to each source report
agent_advisor - AI help desk — which analysis fits your question, and how to read the result

Platform Tools

billing - Usage and credit management
account_link - Link to the right account page for anything not doable in chat
about - Platform documentation and info — how it works, tiers, usage

Browse the catalog yourself, without an account: curl -X POST https://api.mcpanalytics.ai/mcp/discover -H 'Content-Type: application/json' -d '{"jsonrpc":"2.0","method":"tools/list","id":1,"params":{}}' Discovery returns the 15 tools that work pre-auth; billing, connectors_list, and connectors_query appear once you connect with a key or via OAuth.

Features

Natural Language Interface

Just describe what you need:

code

"What drives our revenue growth?"
"Find customer segments in our data"
"Forecast next quarter's sales"
"Did our marketing campaign work?"

Comprehensive Analysis Suite

Statistical Methods

Regression Analysis
Advanced Modeling
Hypothesis Testing
Survival Analysis
Bayesian Methods

Machine Learning

Ensemble Methods
Boosting Algorithms
Neural Networks
Clustering
Dimensionality Reduction

Time Series

Forecasting
Seasonal Analysis
Trend Detection
Multivariate Models
Causal Analysis

Business Analytics

Customer Analytics
Market Analysis
Pricing Models
Predictive Analytics
Experimental Design

Seamless Workflow

graph LR
    A[Ask in Claude/Cursor] --> B[MCP Analytics]
    B --> C[Secure Processing]
    C --> D[Interactive Report]
    D --> E[Share Results]

Example Usage

Basic Regression

code

User: "I have a CSV with house prices. Can you predict price based on size and location?"
Claude: [Runs linear regression, provides R², coefficients, and diagnostic plots]

Customer Segmentation

code

User: "Segment my customers in sales_data.csv into meaningful groups"
Claude: [Performs k-means clustering, creates segment profiles with visualizations]

Time Series Forecasting

code

User: "Forecast next quarter's revenue using our historical data"
Claude: [Applies ARIMA, generates predictions with confidence intervals]

Security & Compliance

Enterprise Security Features

Authentication: OAuth2 via Auth0 with PKCE
Encryption: TLS 1.3 for all data transfers
Processing: Isolated Docker containers per analysis
Data Handling: Ephemeral processing, no persistence
Access Control: OAuth 2.0 scoped permissions with usage limits
Audit Trail: Complete logging for compliance

Privacy & Data Handling

Data Privacy: Ephemeral processing, no data retention
User Rights: Data deletion upon request
Secure Processing: Isolated containers per analysis
Enterprise Options: Contact us for compliance requirements

Read full security documentation →

Architecture

flowchart TB
    subgraph "Client Integration"
        CLI[CLI/SDK]
        Claude[Claude Desktop]
        Cursor[Cursor IDE]
        MCP[MCP Protocol]
    end

    subgraph "API Gateway"
        LB[Load Balancer]
        Auth[OAuth 2.0/Auth0]
        Rate[Rate Limiting]
    end

    subgraph "Processing Layer"
        Router[Request Router]
        Queue[Job Queue]
        Workers[Processing Workers]
        Docker[Docker Containers]
    end

    subgraph "Analytics Engine"
        Stats[Statistical Methods]
        ML[Machine Learning]
        TS[Time Series]
        Report[Report Generation]
    end

    subgraph "Data Layer"
        Cache[Results Cache]
        Storage[Secure Storage]
        Encrypt[Encryption Layer]
    end

    CLI --> LB
    Claude --> LB
    Cursor --> LB
    MCP --> LB

    LB --> Auth
    Auth --> Rate
    Rate --> Router

    Router --> Queue
    Queue --> Workers
    Workers --> Docker

    Docker --> Stats
    Docker --> ML
    Docker --> TS

    Stats --> Report
    ML --> Report
    TS --> Report

    Report --> Cache
    Cache --> Storage
    Storage --> Encrypt

    style Auth fill:#e8f5e9
    style Docker fill:#fff3e0
    style Report fill:#e3f2fd

Performance

Dataset Size: Handles large datasets
Processing Time: Fast cloud-based processing
Secure Infrastructure: Isolated Docker containers
API Access: RESTful API with authentication

Getting Started

Visit our website for pricing and signup →

Documentation

Quick Start Guide - Get running in under a minute
Architecture - How the platform works
Connectors - GA4, GSC, and CSV data sources
Pricing - Credits, tiers, and plans
How Credits Work - The credit model explained
Security - Security & compliance details
Tutorials - Step-by-step guides

Support

Issues: GitHub Issues
Email: support@mcpanalytics.ai
Docs: mcpanalytics.ai/docs
Enterprise: sales@mcpanalytics.ai

Comparison with Other MCP Servers

Feature	MCP Analytics	Google Analytics MCP	PostgreSQL MCP	Filesystem MCP
Use Case	Statistical Analysis	Web Metrics	Database Queries	File Access
Setup Time	30 seconds	OAuth + Config	Connection string	Path config
Data Sources	Any CSV/JSON/URL	GA4 Only	PostgreSQL Only	Local files
Analysis Tools	Full Suite	GA4 Metrics	SQL Only	Read/Write
Machine Learning	✅ Full Suite	❌	❌	❌
Visualizations	✅ Interactive	✅ Dashboards	❌	❌
Shareable Reports	✅	❌	❌	❌

Detailed comparison →

About MCP Analytics

MCP Analytics is built by data scientists and engineers passionate about making advanced statistical analysis accessible through AI assistants. The platform runs deterministic analysis modules — the same data and tool produce the same result every time, unlike LLM code generation.

Testing & Support

Testing Your Connection

After installation, restart your MCP client and look for "MCP Analytics" in the available tools. You should see tools like create_analysis, discover_tools, datasets_upload, etc.

bash

# Test the stdio proxy directly:
MCP_ANALYTICS_API_KEY=mcp_your_key npx -y @mcp-analytics/mcp-analytics
# Should output a "[mcp-analytics] Connected to https://api.mcpanalytics.ai" line with the tool count

Troubleshooting

If MCP Analytics doesn't appear after installation:

Ensure your config file is valid JSON
Restart your MCP client completely
Verify your API key starts with mcp_
Check the client's developer console for errors
Try running the npx command in a terminal to see errors

For support: support@mcpanalytics.ai

Contributing

While the core server is proprietary, we welcome contributions to:

Documentation improvements
Example notebooks and use cases
Bug reports and feature requests
Community tools and integrations

See CONTRIBUTING.md for guidelines.

License

MCP Analytics is a product of PeopleDrivenAI LLC.

This is commercial software. Use of the MCP Analytics service is subject to our:

Ready to transform your data analysis workflow?

Get Started Free | Read Docs | View Demo

Built by MCP Analytics | Powered by R & Python

If MCP Analytics saves you time, a ⭐ on GitHub helps others find it.

Tags: mcp mcp-server model-context-protocol analytics data-analytics shopify-analytics stripe-analytics csv-analysis statistics machine-learning time-series clustering regression business-intelligence claude cursor ai-tools no-code-analytics forecasting customer-analytics

Install

Tools (17)

account_linkaboutagent_advisordatasets_uploaddatasets_listdiscover_toolstools_schemarun_analysis

Remote endpoint

Streamable HTTP

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

url

https://api.mcpanalytics.ai/auth0

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "analytics": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://api.mcpanalytics.ai/auth0"
      ]
    }
  }
}

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

ai.mcpanalytics/analytics MCP Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

account_link

about

agent_advisor

datasets_upload

datasets_list

discover_tools

tools_schema

run_analysis

create_analysis

modify_analysis

build_status

ask_library

reports_list

warehouse

schedules

reports_view

report_cards

MCP Analytics Suite

Overview

Choose Your Depth — Four Tiers

Why MCP Analytics

Quick Start

1. Get an API Key

2. Connect

Option A: npx Install (Recommended)

Option B: Direct API Key (No npm)

Option C: OAuth2 (No API Key)

Browse Tools First (No Account Needed)

3. Start Analyzing

How It Works

The MCP Analytics Workflow

MCP Tools

Analysis

Discovery

Data Management

Connectors

Reporting & Insights

Platform Tools

Features

Natural Language Interface

Comprehensive Analysis Suite

Seamless Workflow

Example Usage

Basic Regression

Customer Segmentation

Time Series Forecasting

Security & Compliance

Enterprise Security Features

Privacy & Data Handling

Architecture

Performance

Getting Started

Documentation

Support

Comparison with Other MCP Servers

About MCP Analytics

Testing & Support

Testing Your Connection

Troubleshooting

Contributing

License

Install

Remote endpoint

Related servers