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bhavya998

analytics-mcp

by bhavya998
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Python
Hybrid

analytics-mcp

Production-grade MCP server for enterprise sales analytics. 6 tools, 3 resources, 2 prompts — all with structured outputs — over a SQLite database. Connects to Claude, OpenCode, Cursor, and any MCP client.

Python FastMCP Tests License

What This Is

A Model Context Protocol (MCP) server that exposes an enterprise sales database to LLM clients. Instead of copy-pasting data into ChatGPT, connect this server and let the AI query, analyze, and visualize your data through structured tools.

Built with the latest MCP patterns (July 2026):

  • FastMCP 3.4 — high-level server framework

  • Structured outputs — Pydantic models define outputSchema for every tool

  • Tool annotations — readOnlyHint lets clients skip confirmations

  • In-memory client testing — no port flakiness, era-neutral

  • Resources — schema introspection, table details, report templates

  • Prompts — reusable analysis templates (sales analysis, customer segmentation)

  • Both transports — stdio (local) + HTTP (remote)

Related MCP server: Acme Operations Assistant

Quick Start

git clone https://github.com/bhavya998/analytics-mcp.git
cd analytics-mcp
uv sync

# Initialize database (200 customers, 25 products, 2000 orders)
uv run analytics-mcp init

# Start server (stdio for local MCP clients)
uv run analytics-mcp serve

# Or HTTP transport for remote access
uv run analytics-mcp serve --transport http --port 8000

Connect to Claude Desktop / OpenCode

Add to your MCP client config:

{
  "mcpServers": {
    "analytics": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/analytics-mcp", "analytics-mcp", "serve"]
    }
  }
}

Then ask: "Show me the top 5 products by revenue and analyze the monthly sales trend"

Tools

Tool

Description

Annotations

query_database

Run parameterized SELECT queries against the database

readOnlyHint

get_revenue_report

Revenue by category/region/tier/status/month with profit analysis

readOnlyHint

get_customer_profile

360-degree customer view: orders, LTV, favorite category, recent activity

readOnlyHint

get_top_products

Leaderboard by revenue or quantity, optional category filter

readOnlyHint

analyze_sales_trend

Time-series with period-over-period growth rates

readOnlyHint

get_regional_performance

Ranked regional performance with revenue, orders, customers, AOV

readOnlyHint

Resources

URI

Description

schema://database

Full database schema (all tables, columns, types, row counts)

schema://tables/{table_name}

Detailed table schema with sample rows

report://templates

Available report templates and usage examples

Prompts

Prompt

Description

sales_analysis

Comprehensive sales analysis with focus area (overall/category/region/customer)

customer_segmentation

Segment customers into Champions/At Risk/New/Dormant with retention strategies

Database Schema

customers (200 rows)
  id, name, email, company, region, tier, signup_date, lifetime_value

products (25 rows)
  id, name, category, price, cost, stock

orders (2000 rows)
  id, customer_id, employee_id, order_date, status, total

order_items (5000+ rows)
  id, order_id, product_id, quantity, unit_price

employees (15 rows)
  id, name, role, region, hire_date

Testing

make test         # 43 tests: database, tools, full MCP client integration
make lint         # ruff

Suite

Tests

Pattern

test_database.py

8

DB init, query validation, schema introspection

test_tools.py

17

Direct tool function calls (all 6 tools)

test_server_client.py

12

Full MCP protocol via in-memory Client

Tests use the modern in-memory Client(mcp) pattern — no ports, no subprocesses, era-neutral.

Tech Stack

Layer

Technology

MCP Framework

FastMCP 3.4 (Prefect)

Protocol

MCP 1.28 (Streamable HTTP + stdio)

Database

SQLite with seeded enterprise data

Validation

Pydantic v2 (structured tool outputs)

CLI

Typer + Rich

Testing

pytest + pytest-asyncio + FastMCP Client

Project Structure

analytics-mcp/
├── src/analytics_mcp/
│   ├── server.py         MCP server: 6 tools, 3 resources, 2 prompts
│   ├── database.py       SQLite setup + schema + seed data (2000+ orders)
│   ├── schemas.py        Pydantic models for structured outputs
│   └── cli.py            CLI (serve, init, inspect)
├── tests/                43 tests (database, tools, client integration)
├── data/                 SQLite database (auto-generated, gitignored)
├── Makefile
└── pyproject.toml

License

MIT

Install Server
A
license - permissive license
A
quality
C
maintenance

How are these scores calculated?

Maintenance

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

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