DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to map a math paper's main results before reading with Papergraph MCP

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

  • Top pick
  • Research
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Who it is forA graduate student or researcher who has a new math paper and wants to know which theorem to read first.

The problemDense papers hide which results matter and what outside material they rely on, so readers often start in the wrong place.

The outcomeA Paper Map listing the paper structure, main-result candidates, and external reading risks, so the reader can choose a theorem with source evidence.

What you need first

  • 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

Steps

  1. Run uvx --from papergraph-mcp==1.2.3 papergraph-mcp doctor to confirm the install works.
  2. 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)
  3. 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)
  4. 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.

Prompt for your agent

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.

Caveat. PaperGraph does not verify proofs, so the Paper Map ranks candidates for reading and does not confirm which result is correct.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page