context-bridge

context-bridge

Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.

Category
访问服务器

README

<p align="center"> <img src="./assets/logo.png" width="200" height="200" style="border-radius: 50%;" alt="Context Bridge Logo"> </p>

<p align="center"> <a href="https://www.npmjs.com/package/@elkraps/context-bridge"> <img src="https://img.shields.io/npm/v/%40elkraps%2Fcontext-bridge?color=blue" alt="NPM Version"> </a> </p>

Context Bridge MCP

Context Bridge is a Model Context Protocol (MCP) server designed to provide Large Language Models (LLMs) with secure, read-only access to local documentation. It acts as an intermediary layer, allowing agents to scan directories, search for keywords, and extract content from PDF, DOCX, Markdown, and plain text files directly from the host filesystem.

This tool resolves the context isolation problem by enabling agents to reference large local knowledge bases without requiring file uploads or manual copy-pasting.

Features

  • File System Scanning: recursively list documents with metadata (size, modification date) to understand the knowledge base structure.
  • Content Extraction: parse and extract text from binary formats (PDF, DOCX) and text-based formats (Markdown, TXT).
  • Semantic Search: perform keyword-based search across multiple files to locate relevant information snippets.
  • Safety: operates in read-only mode to prevent accidental data modification.

Available Tools

  • list_documents: Scans a directory for supported files (PDF, DOCX, MD, TXT).

    • path (string, optional): Absolute path to the directory. Defaults to current.
    • recursive (boolean, optional): Enable subdirectory scanning.
  • read_document: Extracts text content from a file. Handles binary conversion automatically.

    • path (string, required): Absolute path to the file.
  • search_documents: Performs case-insensitive keyword search with context snippets.

    • query (string, required): Search term.
    • path (string, optional): Directory scope.
    • recursive (boolean, optional): Enable subdirectory search.

Getting Started

Local Installation (Source)

  1. Clone the repository:

    git clone https://github.com/elkraps/context-bridge-mcp.git
    cd context-bridge
    
  2. Install dependencies and build:

    npm install
    npm run build
    
  3. Client Configuration: Add the server configuration to your MCP-compatible client's settings file. For example:

    {
      "mcpServers": {
        "context-bridge": {
          "command": "node",
          "args": ["/ABSOLUTE/PATH/TO/context-bridge/build/index.js"]
        }
      }
    }
    

NPX Usage

Configure your MCP client to run the server directly via npx:

{
  "mcpServers": {
    "context-bridge": {
      "command": "npx",
      "args": ["-y", "@elkraps/context-bridge"]
    }
  }
}

Usage

To effectively utilize this tool, you must explicitly direct the agent to interface with the local documentation using the context-bridge terminology.

System Prompt Configuration

To ensure the agent prioritizes this MCP server over generic shell commands (like ls or cat), add the following instruction to your System Prompt, .cursorrules, or custom instructions:

"You have access to local documentation via the context-bridge tools. ALWAYS use list_documents, read_document, and search_documents to explore, read, or search files. Do not use shell commands for documentation tasks."

Interaction Examples

Correct Prompting:

"Please analyze the system architecture described in my documentation folder at /Users/username/projects/docs using context-bridge. List the available files first."

"Search for 'authentication protocols' within the local documentation using context-bridge and summarize the findings."

"Read the file /Users/username/projects/docs/api-spec.pdf using context-bridge and generate a Python client based on it."

Supported File Types

  • PDF (.pdf): Text extraction only (OCR not supported).
  • Microsoft Word (.docx): Text extraction.
  • Markdown (.md): Native text reading.
  • Plain Text (.txt): Native text reading.

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