MCP Finder MCP Server

MCP Finder: My Personal Tool Router

I built this project to solve a simple problem: I have too many MCP tools and I don't know which one to use.

Instead of manually searching through documentation or guessing which server has the right tool, I created this "router". I just ask it a question in plain English, and it tells me exactly which tool to use.

How It Works

When I ask a question like "Scan my docker images for security issues", here is what happens behind the scenes:

1. Intent Analysis: First, the system looks at my query to understand what I want. It checks if I'm asking for something "local", "free", or if I need a specific type of tool (like "security" or "database").
2. Search: It searches my catalog (mcpfinder.sqlite) using two methods:
   • Keywords: Matches words in my query to tool descriptions.
   • Meaning (Embeddings): Matches the concept of my query to tools (so "picture" matches "image").
3. Scoring: It gives every tool a score based on how well it matches. It even gives a bonus to tools that work best with my current editor (like Cursor).
4. AI Reranking: Finally, it sends the top candidates to a small AI model (GPT-4o-mini). The AI looks at them like a human would, picks the best one, and explains why.

The Data Flow

I keep it simple. My data lives in a CSV file, and the app reads from a fast SQLite database.

1. Source of Truth: db.csv
   • This is where I manually add or edit tools. It's just a spreadsheet.
2. The Database: mcpfinder.sqlite
   • The app doesn't read the CSV directly (it's too slow). Instead, I load the data into this SQLite database.
   • The file mcp_suggester/load_mcp_csv_to_sqlite.py handles this conversion.

How I Set It Up

1. Prerequisites

I need Python installed. Then I install the dependencies:

pip install -r requirements.txt

2. Environment Variables

I need to tell the app where my database is and give it an OpenAI key (for the "smart" parts like understanding meaning and reranking).

$env:MCP_CATALOG_DB = ""
$env:OPENAI_API_KEY = "sk-..."

3. Running It

I can run the server directly to test it:

python -m mcp_suggester.server

4. Using the Web UI (Optional)

If you want a visual interface to test queries, you can use the Streamlit app:

streamlit run ui_app.py

This opens a browser window where you can:

• Type your query in a text box
• Adjust settings with sliders (number of results, candidate pool size)
• See the results with nice formatting, reasoning, and copy-pasteable examples

No need to run the server separately—the UI app calls the logic directly.

Using It in Cursor

This is the best part. I connect this "router" to Cursor so I can use it while I code.

1. Open Cursor Settings > MCP.
2. Add a new MCP server:
   • Type: command
   • Command: python
   • Args: -m mcp_suggester.server
   • Env: Add my PYTHONPATH, MCP_CATALOG_DB, and OPENAI_API_KEY.

Now, in Cursor Chat, I just type:

"Find a tool to deploy this app to Kubernetes"

And it responds with the exact tool I need (e.g., Helm -> deploy_application).

Project Structure

• mcp_suggester/: The core logic.
  • server.py: The entry point that Cursor talks to.
  • scoring.py: The math that ranks tools.
  • intent.py: The logic that figures out what I want.
• db.csv: My list of tools.
• mcpfinder.sqlite: The database the app actually reads.

How I Measure Quality

I don't just guess if the search is working. I have a test suite in the evaluation/ folder to prove it.

• eval_dataset.csv: This is my "exam" for the system. It contains 27 real-world questions (like "Find a tool to deploy to Kubernetes") and the exact tool that should be the top answer.
• evaluate.py: This script runs those questions through three different strategies to see which one wins.

The Results

When I run the benchmark (python evaluation/evaluate.py), here is what I typically see:

1. Keyword Search Only: ~60% accuracy.
   • It fails when I use different words than the tool description (e.g., asking for "pictures" when the tool says "images").
2. Hybrid Search (Keywords + Meaning): ~55-60% accuracy.
   • Better at understanding concepts, but sometimes gets confused by similar tools.
3. Hybrid + AI Reranking: ~70%+ accuracy.
   • This is why the LLM is essential. It closes the gap by "thinking" about the results.

What the AI Actually Does

You might wonder, "Why do I need an LLM? Can't I just search?"

When the system finds 20 possible tools, it sends the top 3-5 to the LLM (GPT-4o-mini) with a very specific prompt. The LLM is not just summarizing. It is acting as a judge.

Here is exactly what it does for every single query:

1. It Reads the Documentation: It looks at the tool's description, arguments,