Harvest MCP server

harvest-mcp

Plain-English topic in → 12 experts fan out over Hugging Face + GitHub in parallel → coverage-scored training pack JSON out in ~30 seconds.

No API keys. No Bridge. No account. It works out of the box.

Harvest is a training-data scout: you describe what you want to train on in plain English ("image classification", "medical question answering"), and 12 domain experts — code, math, science, language, vision, audio, medical, law, knowledge, safety, affect, systems — each sweep Hugging Face datasets and GitHub repos with their own curated queries. The local mixer then composes the hits into a balanced, coverage-scored training library pack (flare-harvest-library v1 JSON) you can hand to any training pipeline.

Install

npx github:The-Warl0ck/harvest-mcp

Or add it to Claude Desktop (claudedesktopconfig.json):

{
  "mcpServers": {
    "harvest": {
      "command": "npx",
      "args": ["github:The-Warl0ck/harvest-mcp"]
    }
  }
}

With optional tokens for higher rate limits:

{
  "mcpServers": {
    "harvest": {
      "command": "npx",
      "args": ["github:The-Warl0ck/harvest-mcp"],
      "env": {
        "HF_TOKEN": "hf_...",
        "GITHUB_TOKEN": "ghp_..."
      }
    }
  }
}

Requirements: Node.js ≥ 20.

Example session

You: find me training data for a small vision-language model

harvest.search { "topic": "vision language instruction tuning" }

→ ~40 hits: LLaVA-NeXT data, the Cauldron, DOCCI, LLaVA repo… plus the

12-expert atlas sweep for breadth (code, math, safety…)

harvest.compose { "goal": "small vision-language model", "catalog": […] }

→ { "title": "small vision-language model", "picks": [ { "id": "lmms-lab/LLaVA-NeXT-Data", "expert": "vision", "why": "Anchor Vision on Hugging Face" }, … ], "rationale": "Local mixer staged 22 items across 12/12 experts. No mouth required.", "engine": "local" }

harvest.pack { "goal": "small vision-language model", "name": "VLM Mix v1", "catalog": […] }

→ flare-harvest-library v1 JSON with coverage: { filled: 12, totalExperts: 12, balance: 0.94 }

and per-item ingest: { type: "hf-dataset" | "github-repo", ref } pointers.

Tools

catalog items are { id, source, expert, description? } — the objects harvest.search returns drop straight in.

Rate limits

Everything hits the public Hugging Face and GitHub APIs. Unauthenticated, you get:

• Hugging Face: generous anonymous quota on /api/datasets (occasional 429s on bursts; results are cached 12 min in-process)
• GitHub: 60 requests/hour per IP for search — the 12-expert sweep is the hungriest call

For real use, set HFTOKEN / GITHUBTOKEN (free accounts) via env or the per-call hftoken / ghtoken args. See .env.example.

Pack format

Packs are flare-harvest-library v1 JSON — see PACK-FORMAT.md.

How it works

Shortened. The full README is on GitHub.