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What you can do with Papergraph MCP

PaperGraph MCP is a Python tool that turns arXiv papers, local LaTeX projects, and born-digital PDFs into a local, evidence-grounded workspace of math results, proofs, and dependencies for AI agents and readers.

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lotchuazzz-crypto/papergraph-mcp on GitHub

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What do you want to do?

4 use cases

  • Map a math paper's main results before reading

    Get a Paper Map of structure, main-result candidates, and external reading risks before you pick a theorem.

    View prompt
    Use the papergraph-mcp repo to build a Paper Map for the paper at <paper source> in the workspace at <workspace path>. Return the main-result candidates and external reading risks as a ranked list. Verify that each candidate cites a source slice from the paper.

  • Trace a theorem's dependencies before reading its proof

    See direct and recursive dependencies, shared prerequisites, and unknowns for one chosen result.

    View prompt
    Using the papergraph-mcp repo, run get-dependency-reading on the paper <paper id> in workspace <workspace> for result <result id>. Report the direct dependencies, shared prerequisites, and unknowns. Verify that every listed dependency links to a source slice and that unknowns are not presented as established.

  • Find public full text for a DOI paper

    Discover public PDF candidates for a DOI and import the root paper when one eligible copy exists.

    View prompt
    Use the papergraph-mcp repo to discover public full-text candidates for DOI <doi>. Return the candidates with their sources and do not import anything until the user chooses one. Verify that no file was downloaded during discovery.

  • Export a Reading Report and cross-paper reading plan

    Save checkpoints and notes, then export Markdown reports that summarize your reading across papers.

    View prompt
    Using the papergraph-mcp repo and workspace <workspace path>, export a Reading Report and a Cross-Paper Reading Plan. Return both as Markdown files and check that each open question in the report appears in the workspace state.

How to use Papergraph MCP

  1. Check you have what it needs: uv installed (see the uv getting-started guide linked in the README); An arXiv ID, a local LaTeX project with its main .tex file, or a born-digital PDF of a math paper; A workspace database location outside the repository.
  2. Open lotchuazzz-crypto/papergraph-mcp on GitHub and read the README and license.
  3. Run uvx --from papergraph-mcp==1.2.3 papergraph-mcp doctor to confirm the install works.
  4. Run uvx --from papergraph-mcp==1.2.3 papergraph-mcp plan-starter-project to preview and validate your input files before anything is written. (inferred, check the README)
  5. Run uvx --from papergraph-mcp==1.2.3 papergraph-mcp bootstrap-reading-project after reviewing the preview to create the workspace. (inferred, check the README)
  6. Run uvx --from papergraph-mcp==1.2.3 papergraph-mcp get-paper-map --workspace WORKSPACE --paper-id PAPER_ID and review the main-result candidates.

These steps come from our top use case, Map a math paper's main results before reading, which also has a copy-paste agent prompt.

Papergraph MCP alternatives · Best AI repos for Research

About this repo

Requirements

  • uv installed (see the uv getting-started guide linked in the README)
  • An arXiv ID, a local LaTeX project with its main .tex file, or a born-digital PDF of a math paper
  • A workspace database location outside the repository
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Questions about Papergraph MCP

What is Papergraph MCP?

PaperGraph MCP is a Python tool that turns arXiv papers, local LaTeX projects, and born-digital PDFs into a local, evidence-grounded workspace of math results, proofs, and dependencies for AI agents and readers. It is an open-source project on GitHub: https://github.com/lotchuazzz-crypto/papergraph-mcp.

Who is Papergraph MCP for?

Graduate students, mathematicians, and researchers who read dense math papers, and AI agents that need source-backed reading steps.

What can I do with Papergraph MCP?

4 use cases so far, for example: Map a math paper's main results before reading; Trace a theorem's dependencies before reading its proof; Find public full text for a DOI paper. Each has steps and a copy-paste agent prompt.

What do I need to run Papergraph MCP?

uv installed (see the uv getting-started guide linked in the README); An arXiv ID, a local LaTeX project with its main .tex file, or a born-digital PDF of a math paper; A workspace database location outside the repository.

What are alternatives to Papergraph MCP?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Papergraph MCP safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.