What is Mcp Search

MCP Search is a Python project that offers a simple search interface for querying documentation, enabling users to easily find relevant information.

Use cases

Use cases for MCP Search include searching through software documentation, retrieving information from academic papers, and assisting in customer support by providing quick answers from extensive knowledge bases.

How to use

To use MCP Search, install it via pip with the command: pip install 'git+https://github.com/Alviner/mcp-search@main'. Configure it using environment variables for documentation path, caching, embedding models, and OpenAI API settings.

Key features

Key features of MCP Search include customizable configuration through environment variables, support for various logging formats and levels, and integration with OpenAI models for enhanced search capabilities.

Where to use

MCP Search can be used in various fields such as software development, technical documentation, and any area requiring efficient information retrieval from large text datasets.

MCP Search

MCP Search is a Python project that provides a simple search interface for querying documentation.

Installation

Can be installed with uv

  uv pip install "git+https://github.com/Alviner/mcp-search@main"

Configuration

MCP Search can be configured using the following environment variables:

usage: mcp-search [-h] --docs-path DOCS_PATH [--cache-folder CACHE_FOLDER] [--embedding-model EMBEDDING_MODEL] [--chunk-size CHUNK_SIZE] [--chunk-overlap CHUNK_OVERLAP]
                  [--logs-format {color,disabled,journald,json,plain,rich,rich_tb,stream,syslog}] [--logs-level {critical,debug,error,info,notset,warning}] --openai-api-key
                  OPENAI_API_KEY [--openai-base-url OPENAI_BASE_URL] [--openai-model OPENAI_MODEL]

options:
  -h, --help            show this help message and exit
  --docs-path DOCS_PATH
                        [ENV: MCP_DOCS_PATH]
  --cache-folder CACHE_FOLDER
                        (default: /tmp/mcp-search) [ENV: MCP_CACHE_FOLDER]
  --embedding-model EMBEDDING_MODEL
                        (default: sentence-transformers/all-MiniLM-L6-v2) [ENV: MCP_EMBEDDING_MODEL]
  --chunk-size CHUNK_SIZE
                        (default: 5096) [ENV: MCP_CHUNK_SIZE]
  --chunk-overlap CHUNK_OVERLAP
                        (default: 1024) [ENV: MCP_CHUNK_OVERLAP]

Logs options:
  --logs-format {color,disabled,journald,json,plain,rich,rich_tb,stream,syslog}
                        (default: color) [ENV: MCP_LOGS_FORMAT]
  --logs-level {critical,debug,error,info,notset,warning}
                        (default: info) [ENV: MCP_LOGS_LEVEL]

OpenAI options:
  --openai-api-key OPENAI_API_KEY
                        [ENV: MCP_OPENAI_API_KEY]
  --openai-base-url OPENAI_BASE_URL
                        (default: https://api.studio.nebius.com/v1/) [ENV: MCP_OPENAI_BASE_URL]
  --openai-model OPENAI_MODEL
                        (default: meta-llama/Meta-Llama-3.1-70B-Instruct-fast) [ENV: MCP_OPENAI_MODEL]

Zed Configuration

{
"context_servers": {
    "mcp-search": {
      "command": {
        "path": "mcp-search",
        "env": {
          "MCP_OPENAI_API_KEY": "<OPENAI_API_KEY>"
        },
        "args": [
          "--docs-path",
          "<DOCS_PATH>"
        ]
      }
    }
  }
}

Contributing

To contribute to MCP Search, please fork the repository and submit a pull request with your changes.

License

MCP Search is licensed under the MIT License.