Skip to main content
Glama
tkarim45

mcp-analytics-server

by tkarim45

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.


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.


Related MCP server: Gestao-MCP

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"] }
  }
}

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.


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.


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

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).

A
license - permissive license
-
quality - not tested
C
maintenance

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tkarim45/mcp-analytics-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server