Fast Context MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Fast Context MCPfind the user login handler in the auth module"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Fast Context MCP
AI-driven semantic code search via reverse-engineered Windsurf protocol (Python implementation).
Overview
Fast Context MCP provides an AI-powered semantic code search tool through the Model Context Protocol (MCP). It leverages a reverse-engineered Windsurf protocol to deliver intelligent code context retrieval for LLMs and development workflows.
Related MCP server: code-rag-mcp
Features
AI-Powered Semantic Search: Natural language queries to find relevant code
MCP Server Integration: Compatible with MCP-enabled clients (Claude Desktop, etc.)
Protobuf Protocol: Efficient binary communication with Windsurf API
Tree-based Context: Includes directory structure for better code understanding
Multi-language Support: Works with any codebase (Python, JavaScript, Go, etc.)
Installation
From PyPI (Recommended)
pip install fast-context-mcpFrom Source
git clone https://github.com/YOUR_USERNAME/fast-context-mcp-py.git
cd fast-context-mcp-py
pip install -e .Usage
As an MCP Server
Add to your Claude Desktop configuration (claude_desktop_config.json):
{
"mcpServers": {
"fast-context": {
"command": "python",
"args": ["-m", "fast_context_mcp.server"]
}
}
}Programmatic Usage
from fast_context_mcp.search import search_with_content
result = search_with_content(
query="Find the authentication middleware",
project_root="/path/to/your/project"
)
print(result)Available Tools
search_code
Search for relevant code in a codebase using AI-powered semantic search.
Parameters:
query(string): Natural language description of what you're looking forproject_root(string): Absolute path to the project root directory
Returns: JSON-formatted search results with relevant file paths and line ranges.
Architecture
fast_context_mcp/
├── core.py # Core search implementation & API communication
├── search.py # Search orchestration and result formatting
├── server.py # MCP server implementation
├── protobuf.py # Protobuf encoding/decoding
├── executor.py # Tool execution with context management
└── rg_installer.py # Ripgrep auto-installerProtocol Details
The project implements a reverse-engineered version of Windsurf's internal protocol:
Connect Frame: Binary protobuf handshake with magic bytes (
0x0001)Session Management: UUID-based session tracking
Tool Definitions: JSON Schema-based tool specifications
Response Streaming: Chunked protobuf responses with gzip compression
Development
Setup
# Install development dependencies
pip install -e ".[dev]"Running Tests
pytestLinting
ruff check .
ruff format .License
MIT License - see LICENSE file for details.
Acknowledgments
Inspired by Windsurf's Cascade feature
Built with the Model Context Protocol
Disclaimer
This project is a reverse-engineered implementation for educational purposes. It is not affiliated with or endorsed by Codeium/Windsurf.
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