mcp-analytics-server

tkarim45/mcp-analytics-server
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Install to Claude Code

This server doesn't publish a one-line install command. Follow the setup in the source repository.

Summary

Enables LLMs to interact with a SQLite e-commerce database via safe, typed MCP tools with read-only guards and auth-gated mutations, plus a Claude agent for answering business questions.

README.md

πŸ”Œ MCP Analytics Server β€” talk to your database through the Model Context Protocol

An MCP server that exposes a SQLite e-commerce database to any MCP client (Claude Desktop, Cursor, the Anthropic SDK, …) as safe, structured, schema-typed tools β€” plus a Claude agent that answers business questions by calling those tools. Built on the standard mcp Python SDK (FastMCP). Includes a read-only SQL guard, an auth-gated mutation, and a schema resource.

MCP is the 2026 standard for connecting agents to tools and data β€” 10,000+ servers already published, native in ChatGPT, Claude, Cursor, Copilot, and VS Code. This repo is a clean, production-shaped example of the thing teams keep needing: a governed gateway between an LLM and a database β€” not raw SQL access, but typed tools with guards and authorization.

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What it exposes

| Tool | Kind | Notes | |---|---|---| | list_tables | read | tables in the database | | describe_table(table) | read | columns, types, primary keys | | run_query(sql) | read | read-only ad-hoc SELECT/WITH β€” single statement, mutation keywords blocked, row-capped | | top_products(limit) | read | best-sellers by units (completed orders) | | revenue_summary() | read | revenue + order/customer counts | | create_support_ticket(...) | write | auth-gated β€” requires the write API key | | schema://database | resource | the full SQL schema |

The split is the senior point: a bash/raw-SQL tool hands the model unbounded power; these are dedicated, typed tools the host can validate, gate, and audit. run_query is read-only and capped; the only mutation is behind an API key.

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Two ways to drive it

PY=~/miniconda3/envs/personal/bin/python
$PY -m pip install -e ".[all]"

# 1) OFFLINE β€” exercise the live MCP protocol end-to-end, no API key, no LLM:
$PY -m mcp_analytics.client demo
#   [mcp] connected β€” 6 tools: list_tables, describe_table, run_query, ...
#   [mcp] revenue_summary -> {"revenue": 184293.5, "orders": 968, "customers": 188}
#   [mcp] top_products(3) -> [{"name": "Product 7", "category": "Books", "units": 142}, ...]
#   [mcp] blocked mutation -> tool error: only SELECT / WITH queries are allowed

# 2) AGENT β€” let Claude answer a question by calling the tools (needs a key):
export ANTHROPIC_API_KEY=sk-ant-...
$PY -m mcp_analytics.client ask "Which country has the most customers, and what's total revenue?"

The server itself runs over stdio (python -m mcp_analytics.server) β€” point Claude Desktop / Cursor / any MCP client at that command and the tools appear.

Use it from Claude Desktop / Cursor

{
  "mcpServers": {
    "analytics": { "command": "python", "args": ["-m", "mcp_analytics.server"] }
  }
}

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Architecture

  MCP client (Claude Desktop Β· Cursor Β· Anthropic SDK Β· this client.py)
        β”‚  JSON-RPC over stdio
        β–Ό
  FastMCP server  (server.py)  ──tools──►  list_tables Β· describe_table Β· run_query
        β”‚                                  top_products Β· revenue_summary
        β”‚                                  create_support_ticket (auth-gated)
        β”‚  ──resource──►  schema://database
        β–Ό
  db.py   pure, testable query layer  ──►  SQLite e-commerce DB (customers Β· products
          read-only guard Β· auth Β· seed                          orders Β· order_items Β· tickets)

Tool logic lives in db.py (unit-tested without the protocol); server.py is the thin FastMCP adapter. The Claude agent (client.py) converts the server's MCP tools to Anthropic tools via anthropic.lib.tools.mcp and runs the tool loop.

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Safety & governance

  • Read-only SQL guard β€” run_query accepts only a single SELECT/WITH, rejects

INSERT/UPDATE/DELETE/DROP/ALTER/ATTACH/PRAGMA/CREATE, blocks multi-statement injection, and caps rows. Executed on a mode=ro SQLite connection as defence-in-depth.

  • Auth-gated mutation β€” create_support_ticket requires the write API key

(MCP_WRITE_API_KEY); every other tool is read-only.

  • Validated inputs β€” unknown tables/customers raise typed errors surfaced to the agent.

These guards are the security-critical surface and are covered by the test suite.

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Repo layout

mcp-analytics-server/
β”œβ”€β”€ src/mcp_analytics/
β”‚   β”œβ”€β”€ db.py        SQLite schema + seed + pure query layer (read-only guard, auth)
β”‚   β”œβ”€β”€ server.py    FastMCP server: tools + schema resource (stdio)
β”‚   β”œβ”€β”€ client.py    MCP client: offline protocol `demo` + Claude `ask` agent
β”‚   └── config.py    paths, write API key, model
β”œβ”€β”€ tests/           db + query-guard + auth tests (key-free) β€” 11 cases
└── pyproject.toml Β· Dockerfile Β· Makefile Β· .github/workflows/ci.yml

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RΓ©sumΓ© framing

Built an MCP (Model Context Protocol) server exposing a database as governed, schema-typed tools β€” read-only SQL guard, auth-gated mutations, and a schema resource β€” on the standard mcp SDK; plus a Claude agent that answers business questions through it. Demonstrates the 2026 agent-integration standard end-to-end (stdio transport, tool conversion, tool loop).

License

MIT (LICENSE).

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