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).

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:

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):

{
  "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:

{
  "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:

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:

{
  "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:

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

Browse Tools First (No Account Needed)

Explore the full tool catalog before signing up:

# 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

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

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