methods-mcp

Lightweight, on-demand MCP server for structured methods extraction + reproducibility heuristics on academic papers. Built for the Worldwide AI Science Fellowship build challenge.

⚠️ Status: alpha (0.1.x). The tool surface and output shapes may shift between minor versions. Pin to an exact version in production. Bug reports very welcome via GitHub Issues.

Quick demo

$ uvx --from methods-mcp methods-mcp --version
methods-mcp 0.1.6

# In a Claude Code session:
> /mcp add methods-mcp methods-mcp
> Run methods_repro_review on https://arxiv.org/abs/2509.06917

  → tool: methods_repro_review({"input_str":"https://arxiv.org/abs/2509.06917"})

# Returns a MethodsReproReview object. Read `narrative` first — it explains
# everything else in plain English, so no tool-learning is required:

{
  "status": "ok",
  "narrative":
    "Resolved the paper: 'Paper2Agent' by Miao et al. (arxiv 2509.06917, "
    "2025-09-08). Extracted 11 methods steps at moderate self-reported "
    "confidence (0.72) — the procedure is clearly described but hyperparameters "
    "and software versions are absent. Detected the associated code repository "
    "https://github.com/jmiao24/Paper2Agent from an inline link in the paper "
    "text (detection confidence 0.94). The repo scored 0.90/1.00 on the "
    "reproducibility heuristic — verdict: likely reproducible. Present signals: "
    "substantive README, dependencies file, notebooks, figure-plotting script, "
    "recent activity, permissive license. Missing: data/fixtures directory. "
    "Suggested entrypoint: `python make_figures.py`.",
  "metadata":          { ... },   # PaperMetadata
  "methods":           { ... },   # MethodsStructured (null if extraction failed)
  "code_repo":         { ... },   # CodeRepo           (null only if input unresolvable)
  "repro_assessment":  { ... },   # ReproAssessment   (null if no repo detected)
  "errors":            []         # [{step, error_type, message, hint}] on partial
}

methods-mcp is a small, sharply-scoped Model Context Protocol server. It gives any AI agent (Claude Code, Claude Desktop, your Agent SDK script, etc.) eight tools that turn an academic paper URL into:

• canonical metadata,
• best-effort full text + section split,
• a Pydantic-validated structured methods object (steps / reagents / equipment / analyses),
• the paper's associated code repository (best-effort discovery),
• a no-execution-required reproducibility verdict for that repo, and
• a multi-mode summary.

The wedge: heavyweight pipelines like Paper2Agent (Stanford) take 30 minutes to hours to digest a paper into agent-ready tools. methods-mcp is the agent-callable, on-demand complement — every tool returns in seconds, no clone, no execution.

Install

uv add methods-mcp
# or, install globally:
uv tool install methods-mcp
# or, classic pip:
pip install methods-mcp

API keys

For best performance, set both:

... View full README on GitHub