Fastmcp Learning Path Search
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FastMCP Learning Path Vector Search Server
This project provides a Model Context Protocol (MCP) server that enables semantic search over a set of learning resources using FAISS and SentenceTransformers embeddings. It is compatible with Amazon Q and exposes a tool for finding relevant learning resources by query.
Features
- learning_path_search: Finds learning resources relevant to a text query using semantic similarity.
- Fast, local search using FAISS and all-MiniLM-L6-v2 embeddings.
- Deduplicates results by URL.
- Returns titles, snippets, and URLs for matched resources.
Requirements
- Python 3.10+
- Amazon Q CLI
fastmcpPython packagefaiss-cpuPython packagesentence-transformersPython packagepyyamlandnumpyPython packages
Installation
-
Clone this repository:
git clone https://github.com/JoeStech/fastmcp-learning-path-search.git cd fastmcp-learning-path-search -
Create and activate a virtual environment:
python3 -m venv venv source venv/bin/activate -
Install the required packages:
conda is required here for faiss to install properly, which is why there's a two-step package install.
pip install fastmcp sentence-transformersand then run
conda install --file requirements.txt -
Configure Amazon Q to use this MCP server by adding the following to ~/.aws/amazonq/mcp.json:
{ "mcpServers": { "learning_path_server": { "command": "/path/to/your/venv/bin/python3", "args": ["/path/to/your/server.py"], "env": {}, "timeout": 60000 } } }
Usage
-
Start Amazon Q CLI:
q chat -
View available tools by typing:
/tools
This will display all available tools, including your custom MCP tools.
- Use the tool to get back the best learning path based on your query:
ask the learning_path_search tool to search for "are there GitHub hosted Arm runners for windows?" and please provide the best reference URL that it returns
