Jobs MCP Server

A custom MCP (Model Context Protocol) server that exposes a jobs database to any MCP-compatible LLM client (e.g. Claude Desktop). Ask an LLM in plain English to search, filter, and match job openings — it calls this server's tools to answer from real data.

"Find remote Python jobs paying over 20 LPA"
"Show me details for job 5"
"Which jobs match a backend developer with Django and AWS experience?"

Verified working

Real, reproducible output — not mocked — from three different MCP clients talking to this server.

1. Standalone client (python client_demo.py) — launches server.py over stdio and calls every tool:

============================================================
search_jobs(skill='Python', remote=True, min_salary_lpa=15)
============================================================
{
  "job_id": 18,
  "title": "Senior DevOps Engineer",
  "company": "Bright Wave Fintech",
  "location": "Remote",
  "remote": true,
  "min_salary_lpa": 24.0,
  "max_salary_lpa": 33.0
}
...(9 more matches)

============================================================
get_job_details(job_id=9999) -- expected error
============================================================
Error executing tool get_job_details: No job found with job_id=9999
(tool reported an error)

============================================================
match_jobs(candidate_summary="Backend engineer with 4 years of Python, Django, PostgreSQL, and Docker experience, comfortable with AWS.")
============================================================
{
  "job_id": 37,
  "title": "Backend Engineer",
  "company": "Solace Digital",
  "match_score": 24,
  "matched_skills": ["Python", "Django", "PostgreSQL", "AWS"]
}

2. MCP Inspector (Anthropic's official debugging UI) — connected over stdio, ran search_jobs(min_salary_lpa=25), got back a live Tool Result: Success with real rows from jobs.db:

3. Claude Desktop, natural language, no manual tool calls — asked "Find remote Python jobs paying over 20 LPA". Claude called search_jobs, noticed the keyword filter matched job descriptions rather than titles, then made several get_job_details calls on its own to verify each result actually required Python before answering:

All four are real rows from jobs.db, reached via Claude's extension system:

(Settings → Extensions → Developer → Install Unpacked Extension — not a hand-crafted config file.)

All three are reproducible — run python client_demo.py, or npx @modelcontextprotocol/inspector venv/Scripts/python.exe server.py, and you'll get the same shape of result against the same seed data.

Related MCP server: job-search-mcp

Why this project

Almost no junior portfolio has an MCP project. MCP is the emerging standard (2026) for connecting LLM clients to real tools and data sources — this repo demonstrates building the server side of that protocol: the bridge an LLM talks to, not the LLM or chat UI itself.

What it does

• Exposes 4 MCP tools: search_jobs, get_job_details, list_skills, match_jobs

• Exposes 2 MCP resources: jobs://all and jobs://{job_id}

• Backed by a local SQLite database seeded with 40 realistic job listings

• Connects to Claude Desktop over stdio

• Ships a standalone Python client (client_demo.py) so the demo works without any MCP client installed

Tech

MCP Python SDK (FastMCP, @mcp.tool() / @mcp.resource()) · stdio transport · SQLite (stdlib sqlite3) · Python 3.11 · pytest

Architecture

   ┌─────────────────────────┐         ┌──────────────────────────┐
   │   MCP CLIENT             │  stdio  │   YOUR MCP SERVER         │
   │   (Claude Desktop)       │◄───────►│   (FastMCP, server.py)    │
   │                          │  JSON-  │                           │
   │  user: "find remote      │  RPC    │  @mcp.tool search_jobs    │
   │  python jobs > 20 LPA"   │         │  @mcp.tool get_job_details│
   │        │                 │         │  @mcp.tool list_skills    │
   │        ▼ LLM decides to  │         │  @mcp.tool match_jobs     │
   │        call a tool ──────┼────────►│        │                  │
   │                          │         │        ▼ query            │
   │  ◄─── job data ──────────┼─────────┤   ┌─────────────┐         │
   │        │                 │         │   │ SQLite jobs │         │
   │        ▼ LLM writes a    │         │   │   database  │         │
   │        natural answer    │         │   └─────────────┘         │
   └─────────────────────────┘         └──────────────────────────┘

Key idea: the client (Claude Desktop) already has the LLM. This repo builds the server — the tools and data the LLM calls. MCP is the open "plug" standard between them.

How it works

1. The client's LLM reads each tool's name, docstring, and type hints (FastMCP turns these into a JSON schema automatically).

2. The user asks a natural-language question; the LLM decides which tool(s) to call and with what arguments.

3. The client sends a structured JSON-RPC request over stdio; the server runs the corresponding Python function against SQLite.

4. The server returns structured data; the LLM turns it into a natural answer, still grounded in your actual job data.

Project structure

jobs-mcp-server/
├── server.py                 # FastMCP server + @mcp.tool / @mcp.resource
├── src/
│   ├── db.py                 # SQLite connection + schema
│   ├── seed.py                # loads data/jobs_seed.json into SQLite
│   ├── queries.py             # search_jobs / get_job_details / list_skills
│   └── matching.py            # match_jobs ranking (keyword/skill overlap)
├── client_demo.py             # standalone MCP client (no Claude Desktop needed)
├── run_demo.bat               # double-click launcher for client_demo.py (Windows)
├── data/jobs_seed.json        # 40 seeded jobs
├── tests/                     # pytest suite against a temp seeded DB
├── .github/workflows/ci.yml   # runs pytest on every push/PR
├── claude_desktop_config.example.json
├── manifest.json.example      # for Claude apps that use Extensions instead
└── requirements.txt

Run it

python -m venv venv
venv\Scripts\activate        # Windows; `source venv/bin/activate` on macOS/Linux
pip install -r requirements.txt
python -m src.seed           # creates jobs.db and loads 40 sample jobs

Try it without any MCP client

python client_demo.py

This launches server.py as a subprocess over stdio (exactly how Claude Desktop would), lists the tools and resources, and calls each tool with a realistic argument set — including the get_job_details error path for a bad job_id.

On Windows, double-click run_demo.bat to do the same thing without opening a terminal — it activates the venv, runs the demo, and pauses so you can read the output.

Connect to Claude Desktop

On Windows, Claude Desktop's config lives at:

C:\Users\<you>\AppData\Roaming\Claude\claude_desktop_config.json

Add this server under mcpServers (see claude_desktop_config.example.json, adjust paths to your machine — pointing at the venv's python.exe avoids any PATH/interpreter mismatches):

{
  "mcpServers": {
    "jobs": {
      "command": "C:\\path\\to\\jobs-mcp-server\\venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\jobs-mcp-server\\server.py"]
    }
  }
}

Fully restart Claude Desktop. The jobs tools should appear and be callable in chat — try: "Find remote Python jobs paying over 20 LPA".

Newer Claude apps: install as an unpacked extension

Some Claude app builds manage MCP servers as signed "Extensions" instead of reading claude_desktop_config.json directly, and silently ignore/overwrite the mcpServers key above (check Settings → Extensions → Developer — if you see an "Install Unpacked Extension" button, this is the path to use; it's what produced the verified Claude Desktop result above).

1. Copy manifest.json.example to manifest.json and fill in your actual path to venv\Scripts\python.exe.

2. Settings → Extensions → Developer → Install Unpacked Extension → select this project folder.

3. The extension should appear in your extensions list as "Jobs MCP Server" — enable it and start a new chat.

Test with MCP Inspector

MCP Inspector is Anthropic's official debugging UI for MCP servers. It requires Node.js:

npx @modelcontextprotocol/inspector venv/Scripts/python.exe server.py

Open the printed local URL, call each tool from the UI, and confirm the results. (client_demo.py above covers the same ground if Node.js isn't installed.)

Tests

pytest

Covers search_jobs filter combinations (keyword, skill, location, remote, min salary), get_job_details (including the bad-job_id error path), list_skills, and match_jobs ranking behavior — all against a temporary seeded SQLite database, no server/MCP layer involved.

Cost

$0. The server runs locally, the data is a local SQLite file, and Claude Desktop is a free download. No LLM API key is needed — the client's LLM does the reasoning; this server only serves data.

Tools vs. resources

• Tools (search_jobs, get_job_details, list_skills, match_jobs) are actions the LLM actively invokes with arguments.

• Resources (jobs://all, jobs://{job_id}) are readable data a client can pull in directly as context, without an explicit function call.