Company Records

A working Model Context Protocol server in Python that exposes a small company CRM database to AI agents as callable tools, plus a command-line chat agent that answers natural-language questions by calling those tools.

Built on the official MCP Python SDK (mcp) and the official Anthropic SDK.

What is MCP?

The Model Context Protocol is an open standard for connecting AI models to external tools and data. Instead of hard-coding a model's integrations, you run an MCP server that advertises a set of tools (each with a name, description, and JSON input schema). Any MCP client — Claude Desktop, an IDE, or your own agent — can connect, discover those tools, and let the model call them. The server and client talk over a simple transport (here, stdio). The result: one server works with any MCP-compatible client, and the model gets live, structured access to your data instead of guessing from its training set.

What this server does

It serves a fictional B2B CRM dataset (12 companies, 16 contacts, 19 deals, stored in SQLite) through five tools:

Tool	What it does
search_companies	Filter companies by industry, country, and/or minimum headcount
get_company	Full profile for one company, with its contacts and deals
find_contacts	Free-text search over contact name, title, or email
deal_summary	Pipeline totals — counts and dollar value, overall or by stage
top_companies	Rank companies by revenue, employees, or total deal value

The dataset is generated by company_data.py into a local company.db. It's entirely invented and safe to share; swapping in a real database means pointing the tools at different SQL.

How it works

                 stdio (MCP)                     tool calls
   chat.py  ─────────────────►  server.py  ─────────────────►  company.db
  (client)  ◄─────────────────  (server)   ◄─────────────────   (SQLite)
            tool results                       rows

   chat.py also calls the Claude API to decide *which* tools to call.

• server.py defines each tool with the @mcp.tool() decorator. FastMCP turns the function signature and docstring into the tool's input schema and description automatically, and handles the MCP wire protocol.
• chat.py launches the server as a subprocess over stdio, discovers its tools, and runs an agentic loop: it sends your question to Claude along with the tool list, Claude responds with tool calls, chat.py executes them against the server, feeds the results back, and repeats until Claude has a final answer.

Setup

python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r requirements.txt

The database is built automatically on first use. To build (or rebuild) it explicitly:

python company_data.py
# Built .../company.db with {'companies': 12, 'contacts': 16, 'deals': 19}

Running the server

The server speaks MCP over stdio, so you normally run it through a client rather than directly. Two easy ways to poke at it:

MCP Inspector (a browser UI for trying tools by hand — no API key needed):

mcp dev server.py

Claude Desktop — add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "company-records": {
      "command": "python",
      "args": ["/absolute/path/to/mcp-server-demo/server.py"]
    }
  }
}

Restart Claude Desktop and the five tools appear; ask it about the data.

The chat agent (bonus)

chat.py is a self-contained client + agent so you can run the whole thing from one terminal. It needs an Anthropic API key:

export ANTHROPIC_API_KEY=sk-ant-...     # or copy .env.example to .env
python chat.py                           # interactive
python chat.py "Who are the CEOs in the database and what are their companies?"  # one-shot

Example session

$ python chat.py "What's our total closed-won pipeline, and which company has the most deal value?"
Connected to MCP server with 5 tools: search_companies, get_company, find_contacts, deal_summary, top_companies

  · calling deal_summary()
  · calling top_companies(by='deals', limit=1)

Closed-won deals total $13,075,000 across 7 deals. The company with the most
total deal value is Cobalt Mining Group at $7,700,000.

Those figures come straight from the seeded data — deal_summary reports closed_won at $13,075,000, and top_companies(by="deals") ranks Cobalt Mining Group first. The · calling … lines show the actual tool calls Claude chose to make.

Other questions that work well:

• "Which software companies are in the database and how big are they?"
• "Show me the full record for Pinecrest Health."
• "Find every procurement contact."
• "Rank the top 5 companies by revenue."

Project layout

mcp-server-demo/
├── server.py         # MCP server — five tools over the SQLite DB
├── chat.py           # CLI agent: Claude + MCP tool-calling loop
├── company_data.py   # fictional dataset + SQLite builder
├── requirements.txt
└── .env.example

Tech

Python · Model Context Protocol (mcp SDK, FastMCP) · Anthropic API · SQLite