Fast Context

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. All tools are bundled via npm — no system-level dependencies needed (ripgrep via @vscode/ripgrep, tree via tree-node-cli). Works on macOS, Windows, and Linux.

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:

Platform	Path
macOS	~/Library/Application Support/Windsurf/User/globalStorage/state.vscdb
Windows	%APPDATA%/Windsurf/User/globalStorage/state.vscdb
Linux	~/.config/Windsurf/User/globalStorage/state.vscdb

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

Variable	Default	Description
WINDSURF_API_KEY	(auto-discover)	Windsurf API key
FC_MAX_TURNS	3	Search rounds per query (more = deeper but slower)
FC_MAX_COMMANDS	8	Max parallel commands per round
FC_TIMEOUT_MS	30000	Connect-Timeout-Ms for streaming requests
FC_RESULT_MAX_LINES	50	Max lines per command output (truncation)
FC_LINE_MAX_CHARS	250	Max characters per output line (truncation)
WS_MODEL	MODEL_SWE_1_6_FAST	Windsurf model name
WS_APP_VER	1.48.2	Windsurf app version (protocol metadata)
WS_LS_VER	1.9544.35	Windsurf language server version (protocol metadata)

Available Models

The model can be changed by setting WS_MODEL (see environment variables above).

Default: MODEL_SWE_1_6_FAST — fastest speed, richest grep keywords, finest location granularity.

MCP Tools

fast_context_search

AI-driven semantic code search with tunable parameters.

Parameter	Type	Required	Default	Description
query	string	Yes	—	Natural language search query
project_path	string	No	cwd	Absolute path to project root
tree_depth	integer	No	3	Directory tree depth for repo map (1-6). Higher = more context but larger payload. Auto falls back to lower depth if tree exceeds 250KB. Use 1-2 for huge monorepos (>5000 files), 3 for most projects, 4-6 for small projects.
max_turns	integer	No	3	Search rounds (1-5). More = deeper search but slower. Use 1-2 for simple lookups, 3 for most queries, 4-5 for complex analysis.
max_results	integer	No	10	Maximum number of files to return (1-30). Smaller = more focused, larger = broader exploration.

Returns:

1. Relevant files with line ranges
2. Suggested search keywords (rg patterns used during AI search)
3. Diagnostic metadata ([config] line showing actual tree_depth used, tree size, and whether fallback occurred)

Example output:

Found 3 relevant files.

  [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)

grep keywords: authenticate, jwt.*verify, session.*token

[config] tree_depth=3, tree_size=12.5KB, max_turns=3

Error output includes status-specific hints:

Error: Request failed: HTTP 403

[hint] 403 Forbidden: Authentication failed. The API key may be expired or revoked.
Try re-extracting with extract_windsurf_key, or set a fresh WINDSURF_API_KEY env var.

Error: Request failed: HTTP 413

[diagnostic] tree_depth_used=3, tree_size=280.0KB (auto fell back from requested depth)
[hint] If the error is payload-related, try a lower tree_depth value.

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. Directory tree generated at requested depth (default L=3), with automatic fallback to lower depth if tree exceeds 250KB
3. Query + directory tree sent to Windsurf's Devstral model via Connect-RPC/Protobuf
4. Devstral generates tool commands (ripgrep, file reads, tree, ls, glob)
5. Commands executed locally in parallel (up to FC_MAX_COMMANDS per round)
6. Results sent back to Devstral for the next round
7. After max_turns rounds, Devstral returns file paths + line ranges
8. All rg patterns used during search are collected as suggested keywords
9. Diagnostic metadata appended to help the calling AI tune parameters

Technical Details

• Protocol: Connect-RPC over HTTP/1.1, Protobuf encoding, gzip compression
• Model: Devstral (MODEL_SWE_1_6_FAST, configurable)
• Local tools: rg (bundled via @vscode/ripgrep), readfile (Node.js fs), tree (tree-node-cli), ls (Node.js fs), glob (Node.js fs)
• Auth: API Key → JWT (auto-fetched per session)
• Runtime: Node.js >= 18 (ESM)

Dependencies

Package	Purpose
@modelcontextprotocol/sdk	MCP server framework
@vscode/ripgrep	Bundled ripgrep binary (cross-platform)
tree-node-cli	Cross-platform directory tree (replaces system tree)
better-sqlite3	Read Windsurf's local SQLite DB
zod	Schema validation (MCP SDK requirement)

License

MIT