Fastcontext

An MCP server that wraps FastContext-1.0 as a repo-exploration subagent for Claude Code.

Instead of letting Sonnet spend half its context budget grepping around a codebase, you offload that work to a dedicated 4B model trained specifically to explore repos. FastContext issues parallel READ/GLOB/GREP calls, then returns compact file paths + line ranges as grounded citations. Claude Code gets clean context; FastContext does the legwork.

Claude Code (Sonnet) ──explore_repo──▶  fastcontext-mcp  ──READ/GLOB/GREP──▶  your repo
        ▲                                       │
        └──── file:line citations ──────────────┘

Based on Microsoft's FastContext paper: integrating FastContext improves coding agent accuracy by up to 5.5% while reducing main-agent token consumption by up to 60%.

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Requirements

• Python 3.10+
• A running FastContext inference server (SGLang or vLLM, OpenAI-compatible)
• Claude Code

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Setup

1. Serve FastContext locally

You need a GPU with ~6GB VRAM for the 4B model. The 4B-RL variant slightly outperforms 4B-SFT on most benchmarks and is recommended for deployment.

pip install sglang[all]

# SFT variant (default)
./scripts/serve.sh microsoft/FastContext-1.0-4B-SFT

# RL variant (recommended)
./scripts/serve.sh microsoft/FastContext-1.0-4B-RL

Or with vLLM:

pip install vllm
vllm serve microsoft/FastContext-1.0-4B-SFT --tool-call-parser hermes

The server will be available at http://localhost:30000.

2. Install this MCP server

git clone https://github.com/YOUR_USERNAME/fastcontext-mcp
cd fastcontext-mcp
pip install -e .

3. Register with Claude Code

Add to your ~/.claude/claude_desktop_config.json (or project-level .mcp.json):

{
  "mcpServers": {
    "fastcontext": {
      "command": "fastcontext-mcp",
      "env": {
        "FASTCONTEXT_BASE_URL": "http://localhost:30000/v1",
        "FASTCONTEXT_MODEL": "FastContext-1.0-4B-SFT"
      }
    }
  }
}

Restart Claude Code. You should see explore_repo in the available tools.

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Usage

Once registered, Claude Code can call explore_repo automatically, or you can invoke it explicitly:

explore_repo("where is the rate limiting middleware defined")
explore_repo("find all places that call the payment API", repo_root="/path/to/repo")

FastContext will issue several parallel read/search calls internally and return something like:

<final_answer>
- src/middleware/ratelimit.py: lines 12-47
- src/middleware/__init__.py: line 8
- tests/test_ratelimit.py: lines 1-30
</final_answer>

Claude Code then uses those citations as focused context rather than reading the whole codebase.

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Configuration

Env var	Default	Description
FASTCONTEXT_BASE_URL	http://localhost:30000/v1	SGLang/vLLM server URL
FASTCONTEXT_MODEL	FastContext-1.0-4B-SFT	Model name as registered in the server
FASTCONTEXT_MAX_TURNS	8	Max exploration turns before giving up
FASTCONTEXT_MAX_FILE_LINES	300	Max lines returned per READ call

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No GPU? Remote inference

If you don't have a local GPU, you can serve FastContext on a remote machine and point FASTCONTEXT_BASE_URL at it. The MCP server itself is CPU-only and just proxies requests.

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Why not just use Claude Code directly?

You can. But FastContext is trained specifically for the locate-relevant-code task, and it's 4B parameters — it's faster and cheaper per exploration call than routing everything through Sonnet. On large codebases the token savings are significant (the paper reports up to 60% reduction in main-agent tokens).

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License

MIT. FastContext model weights are also MIT licensed by Microsoft.