It intelligently parses your code into structural blocks (like functions and classes), converts them into vector embeddings, and stores them in a local vector database. This allows you to find…
Semantic Context Mcp is an MCP project maintained in the zishengwu/semantic-context-mcp repository. The maintainer describes its purpose as “It intelligently parses your code into structural blocks (like functions and classes), converts them into vector embeddings, and stores them in a local vector database…”. Read the project documentation for the complete scope.
The project provides a local client configuration using fastmcp. Follow the repository’s build and installation steps before copying it, and replace example paths with the paths on your own computer.
The example declares OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL_NAME. Secret-looking values have been replaced with placeholders. Required and optional labels are shown only when the documentation states them.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
{
"mcpServers": {
"Semantic Context MCP Server": {
"command": "fastmcp",
"args": [
"run",
"your_code_base/semantic-context-mcp/vector_search/fast_mcp_server.py:mcp"
],
"env": {
"OPENAI_API_KEY": "YOUR_OPENAI_API_KEY",
"OPENAI_BASE_URL": "your_api_base_url",
"OPENAI_MODEL_NAME": "your_embedding_model_name"
}
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Declared in the maintainer’s client configuration. | Check documentation |
| OPENAI_BASE_URL | Declared in the maintainer’s client configuration. | Check documentation |
| OPENAI_MODEL_NAME | Declared in the maintainer’s client configuration. | Check documentation |
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