papergraph-mcp: PaperGraph MCP turns math papers into evidence-grounded reading maps for AI agents

by lotchuazzz-crypto · MCP Server · ★ 285

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install papergraph-mcp

uvx --from git+https://github.com/lotchuazzz-crypto/papergraph-mcp.git@v0.4.4 papergraph-mcp --version

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsarxivknowledge-graphlatexmathematicsmcpmodel-context-protocolpythonresearch-toolsskills

Quick Facts

Stars285
Forks4
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score72/100
Open Issues1
Last Updated2026-10-18
Created2026-09-02
Platformsmcp, python
Est. Tokens~16k

About papergraph-mcp

PaperGraph MCP PaperGraph turns local or arXiv LaTeX papers into theorem dependency graphs that AI agents can query through MCP. PaperGraph v0.4.4 hardens first-use analysis by making agents verify the running version, validate raw arXiv requests, and report dependency boundaries before interpreting sparse graphs.

Compatible Skills

These tools work well together with papergraph-mcp for enhanced workflows:

  • thesis-skills — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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