mcp-reranker

mcp-reranker

A generic Model Context Protocol (MCP) server for high-accuracy document reranking using sentence-transformers (Cross-Encoder). Ideal for enhancing RAG and AI agent decision-making. sentence-transformers (Cross-Encoder) を使用して文書の関連度を再計算・ソートする汎用 MCP (Model Context Protocol) サーバー。

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README

mcp-reranker

A generic Model Context Protocol (MCP) server that provides document reranking capabilities using sentence-transformers.

This server is designed to be a standalone tool that can be used by any MCP-compatible client (such as Roo Code, Claude Desktop, or custom agents) to improve the precision of RAG (Retrieval-Augmented Generation) or to help agents make better decisions by scoring relevance between a query and multiple candidates.

💡 Proven in Production: This server was extracted as a general-purpose, reusable module from the cingulater project, where it is actively used and running in production.

Features

  • Cross-Encoder Reranking: Utilizes the CrossEncoder model from sentence-transformers for high-accuracy relevance scoring.
  • Project Agnostic: Completely independent of any specific application logic.
  • Customizable Models: Supports various HuggingFace models. You can configure the default model via environment variables (defaults to BAAI/bge-reranker-v2-m3).
  • JSON Output: Returns sorted results in a structured JSON format.

Tools

rerank_documents

Computes relevance scores for a list of documents against a given query and returns them sorted by score.

Arguments:

  • query (string): The search query or the core intent to compare against.
  • documents (array of strings): A list of document descriptions or texts to be ranked.
  • model_name (string, optional): The HuggingFace model identifier. Defaults to the RERANKER_MODEL_NAME environment variable or "BAAI/bge-reranker-v2-m3".

Response Example: A JSON-formatted string:

[
  { "document": "The most relevant document text.", "score": 0.985 },
  { "document": "A partially relevant text.", "score": 0.452 },
  { "document": "Completely irrelevant text.", "score": 0.012 }
]

Installation & Usage

Running with uvx

Add the following to your MCP configuration (e.g., brownie_core_mcp_config.json). You can customize the model used by setting the RERANKER_MODEL_NAME environment variable.

{
  "mcpServers": {
    "mcp-reranker": {
      "command": "uvx",
      "args": [
        "--from",
        "git+[https://github.com/globalpocket/mcp-reranker.git](https://github.com/globalpocket/mcp-reranker.git)",
        "mcp-reranker"
      ],
      "env": {
        "RERANKER_MODEL_NAME": "BAAI/bge-reranker-v2-m3"
      }
    }
  }
}

Development

Prerequisites

  • Python 3.10+
  • uv

Setup

git clone [https://github.com/globalpocket/mcp-reranker.git](https://github.com/globalpocket/mcp-reranker.git)
cd mcp-reranker
uv sync --extra dev

Running Tests

uv run pytest

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