Fast Context MCP

Fast Context MCP

AI-driven semantic code search as an MCP tool — powered by Windsurf's reverse-engineered SWE-grep protocol.

Any MCP-compatible client (Claude Code, Claude Desktop, Cursor, etc.) can use this to search codebases with natural language queries. Ripgrep is bundled automatically via @vscode/ripgrep — no manual installation needed.

How It Works

You: "where is the authentication logic?"
         │
         ▼
┌─────────────────────────┐
│  Fast Context MCP       │
│  (local MCP server)     │
│                         │
│  1. Maps project → /codebase
│  2. Sends query to Windsurf Devstral API
│  3. AI generates rg/readfile/tree commands
│  4. Executes commands locally (built-in rg)
│  5. Returns results to AI
│  6. Repeats for N rounds
│  7. Returns file paths + line ranges
│     + suggested search keywords
└─────────────────────────┘
         │
         ▼
Found 3 relevant files.
  [1/3] /project/src/auth/handler.py (L10-60)
  [2/3] /project/src/middleware/jwt.py (L1-40)
  [3/3] /project/src/models/user.py (L20-80)

Suggested search keywords:
  authenticate, jwt.*verify, session.*token

Prerequisites

• Node.js >= 18
• Windsurf account — free tier works (needed for API key)

No need to install ripgrep — it's bundled via @vscode/ripgrep.

Installation

git clone https://github.com/SammySnake-d/fast-context-mcp.git
cd fast-context-mcp
npm install

Setup

1. Get Your Windsurf API Key

The server auto-extracts the API key from your local Windsurf installation. You can also use the extract_windsurf_key MCP tool after setup, or set WINDSURF_API_KEY manually.

Key is stored in Windsurf's local SQLite database:

2. Configure MCP Client

Claude Code

Add to ~/.claude.json under mcpServers:

{
  "fast-context": {
    "command": "node",
    "args": ["/absolute/path/to/fast-context-mcp/src/server.mjs"],
    "env": {
      "WINDSURF_API_KEY": "sk-ws-01-xxxxx"
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json under mcpServers:

{
  "fast-context": {
    "command": "node",
    "args": ["/absolute/path/to/fast-context-mcp/src/server.mjs"],
    "env": {
      "WINDSURF_API_KEY": "sk-ws-01-xxxxx"
    }
  }
}

If WINDSURF_API_KEY is omitted, the server auto-discovers it from your local Windsurf installation.

Environment Variables

MCP Tools

fast_context_search

AI-driven semantic code search.

Returns:

1. Relevant files with line ranges
2. Suggested search keywords (rg patterns used during AI search)

Example output:

Found 3 relevant files. IMPORTANT: You MUST examine ALL 3 files below.

  [1/3] /project/src/auth/handler.py (L10-60, L120-180)
  [2/3] /project/src/middleware/jwt.py (L1-40)
  [3/3] /project/src/models/user.py (L20-80)

Suggested search keywords:
  authenticate, jwt.*verify, session.*token

extract_windsurf_key

Extract Windsurf API Key from local installation. No parameters.

Project Structure

fast-context-mcp/
├── package.json
├── src/
│   ├── server.mjs        # MCP server entry point
│   ├── core.mjs          # Auth, message building, streaming, search loop
│   ├── executor.mjs      # Tool executor: rg, readfile, tree, ls, glob
│   ├── extract-key.mjs   # Windsurf API Key extraction (SQLite)
│   └── protobuf.mjs      # Protobuf encoder/decoder + Connect-RPC frames
├── README.md
└── LICENSE

How the Search Works

1. Project directory is mapped to virtual /codebase path
2. Query + directory tree sent to Windsurf's Devstral model via Connect-RPC/Protobuf
3. Devstral generates tool commands (ripgrep, file reads, tree, ls, glob)
4. Commands executed locally in parallel (up to FC_MAX_COMMANDS per round)
5. Results sent back to Devstral for the next round
6. After FC_MAX_TURNS rounds, Devstral returns file paths + line ranges
7. All rg patterns used during search are collected as suggested keywords

Technical Details

• Protocol: Connect-RPC over HTTP/1.1, Protobuf encoding, gzip compression
• Model: Devstral (MODEL_SWE_1_5_SLOW)
• Local tools: rg (bundled), readfile, tree, ls, glob
• Auth: API Key → JWT (auto-fetched per session)
• Runtime: Node.js >= 18 (ESM)

Dependencies

License

MIT