methods-mcp 0.1.4

Lightweight, on-demand MCP server for structured methods extraction and reproducibility heuristics on academic papers.

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 0.1.4

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

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

# Returns in seconds:
# - methods: 10 steps, 2 reagents, 3 equipment, 4 analyses (confidence 0.72)
# - code_repo: google-deepmind/alphagenome (detected in paper text, confidence 0.9)
# - repro_assessment: likely-reproducible (0.9/1.0)
#   entrypoint: python src/alphagenome/visualization/plot.py

---

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

Set your Anthropic API key (used by the LLM-driven extraction tools):

export ANTHROPIC_API_KEY=sk-ant-...
# Optional — raises GitHub REST API rate limit for repro assessment:
export GITHUB_TOKEN=ghp_...

Use it from Claude Code

/mcp add methods-mcp methods-mcp

Then in any Claude Code chat:

Take https://arxiv.org/abs/2509.06917 and run methods_repro_review. Summarise what the paper does, the methods steps, and how reproducible the repo looks.

See examples/claude_code_demo.md for more session prompts.

Use it from the Claude Agent SDK

from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient

options = ClaudeAgentOptions(
    mcp_servers={
        "methods-mcp": {
            "type": "stdio",
            "command": "methods-mcp",
            "args": [],
        }
    },
    allowed_tools=["mcp__methods-mcp__methods_repro_review"],
)

async with ClaudeSDKClient(options=options) as client:
    await client.query(
        "Run methods_repro_review on https://arxiv.org/abs/2509.06917 "
        "and tell me whether the repo looks reproducible."
    )
    async for msg in client.receive_response():
        print(msg)

A complete runnable example lives in examples/reflexive_demo.py.

Tools

Tool	What it does
health	Server liveness + config check.
get_paper_metadata(input_str)	Resolve URL / arXiv ID / DOI to canonical metadata. arXiv inputs hit the arXiv export API for title/authors/abstract.
fetch_paper_text(input_str, prefer="auto"|"html"|"pdf")	Full text + section split. Defaults to ar5iv HTML for arXiv papers (cheap, structured), PDF fallback otherwise.
extract_methods(input_str, model=None)	LLM-driven, Pydantic-validated structured methods extraction. Returns {steps, reagents, equipment, analyses, confidence}.
find_code_repo(input_str)	Discover the paper's code repo via paper text → abstract → Papers With Code.
assess_repo_reproducibility(repo_url, paper_id=None)	Heuristic, no-clone reproducibility assessment via the GitHub REST API. Weighted signals (README, deps, fixtures, notebooks, figure scripts, recent maintenance, license) → {verdict, score, recommended_entrypoint}.
summarize_paper(input_str, mode="tldr"|"abstract"|"exec")	LLM summary in three depths.
methods_repro_review(input_str)	Composite — metadata + methods + repo + repro in one call.

All tools return Pydantic v2 models (validated, JSON-serialisable). See src/methods_mcp/schemas.py for the full type surface.

Design notes

• extract_methods uses Anthropic tool-use to coerce the model into emitting an instance of the MethodsStructured Pydantic schema. On validation failure we send one repair message with the validation error and try again before raising.
• assess_repo_reproducibility does not clone or execute anything. It scores the repo from publicly-readable GitHub metadata + the recursive tree listing. This is the deliberate wedge against batch tools that try to actually rerun the paper.
• fetch_paper_text prefers ar5iv HTML over PDF parsing for arXiv papers. Falls back to pypdf for non-arXiv inputs.
• The default model is claude-sonnet-4-6. Override via METHODS_MCP_MODEL env var or per-call model= arg.

Security & limitations

What this server actually does when you install and run it:

• Network calls only to: export.arxiv.org, ar5iv.labs.arxiv.org, arxiv.org (PDFs), api.github.com, paperswithcode.com, api.anthropic.com. No telemetry, no analytics, no phone-home.
• Reads ANTHROPIC_API_KEY (required for LLM tools) and optionally GITHUB_TOKEN from environment variables. These are sent only to Anthropic / GitHub respectively. Never logged, never persisted to disk.
• Writes nothing to your filesystem. No cache directories, no downloaded PDFs, no temp files.
• Executes no user-supplied code. No eval, exec, subprocess, pickle.loads, or shell-outs. The reproducibility tool deliberately does not clone or run repositories — it scores from the GitHub REST API only.

Limitations to be aware of:

• Adversarial papers may produce misleading structured output. The extract_methods tool sends paper text to Claude. A paper containing prompt-injection content could yield wrong (but schema-valid) structured methods. Treat the output as a research aid, not ground truth.
• The reproducibility verdict is a heuristic, not a proof. A high score means the repo looks well-structured for reproduction; it does not guarantee that running the code reproduces the paper. For full validation see Paper2Agent.
• Intended for local stdio use. The HTTP/SSE transports are provided for development convenience but should only be exposed on trusted networks (no SSRF protection beyond what httpx provides).

Reporting issues:

Security issues: please email [e-mail] (also see SECURITY.md). Functional bugs: open a GitHub issue.

Pair with paper-mcp

For broader paper search / citation graph tooling, run paper-mcp (Bhvaik) alongside in the same Claude Code session. paper-mcp does title-keyed search, full-text fetch, citations, and references; methods-mcp adds the structured-methods + reproducibility layer on top. The two were intentionally designed to compose.

Develop locally

git clone https://github.com/FlynnLachendro/methods-mcp
cd methods-mcp
uv sync --extra dev --extra agent

uv run pytest                      # 49 tests, offline (respx-mocked httpx + unittest.mock for Anthropic)
uv run ruff format .
uv run ruff check . --fix
uv run mypy src

uv run methods-mcp --help

Project notes

project-thoughts.md (in this repo) is a running log of what we tried, what stuck, and what we cut while building this. Honest write-up for the WWSF Loom narration.

License

MIT — see LICENSE.

Acknowledgements

Built for the Worldwide AI Science Fellowship inaugural cohort. Thanks to Michael Raspuzzi for the open-ended brief.

Built on:

• FastMCP 3.x — the MCP server scaffold.
• Claude Agent SDK — the agent loop in the demo.
• ar5iv.labs.arxiv.org — clean HTML for arXiv papers.
• Anthropic Claude — the LLM behind structured extraction.