Overleaf (markfu909) MCP Server
@markfu909/overleaf-mcp
npm
v1.0.0
Published by @markfu909 — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.
MCP server for Overleaf integration - read LaTeX files, analyze document structure, and manage Overleaf projects via Git
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 6.3 = 94. What the published surface and source actually contain:
| Points | What was found | Category |
|---|---|---|
| −6.3 | Untrusted input concatenated into a command sink MTC-SRC-009 | injection |
2. Client adoption risk — 94 − 7 = 87. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −6 | capability blast radius (high) — client exposure if the model is manipulated |
| −1 | publisher verification (public source) — no provenance, but the source is public and inspectable |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 3
high Shell/command execution in server code (overleaf-mcp-server.js)MTC-SRC-002
In the server's implementation (`overleaf-mcp-server.js:9`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: import { spawn } from 'child_process'; import { readFile, access, mkdir } from 'fs/promises'; import { promisify } from
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server overleaf-mcp-server.js
medium Untrusted input concatenated into a command sink (overleaf-mcp-server.js)MTC-SRC-009
In the server's implementation (`overleaf-mcp-server.js:67`): A shell/process command assembled from concatenated or interpolated values is command injection when any part is attacker-influenced — the OWASP canonical RCE flow. Verify what reaches the interpolated value. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: repo exists await exec(`test -d "${this.repoPath}/.git"`); // Pull latest changes const { stdout } = a
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server overleaf-mcp-server.js
low Unconstrained path parameter "filePath" on "read_file"MTC-CAP-008
Tool "read_file" takes a path parameter "filePath" with no constraint. Without a canonicalize-and-contain check (not visible statically), this permits ../ traversal outside the intended root.
Fix: Resolve and verify the path stays within an allowed root; reject traversal sequences.
Location: tool read_file · inputSchema.properties.filePath
Tools 6
Each tool and what it can reach — statically extracted from the published source.
list_filesreads sensitive dataread_filereads sensitive dataget_section_contentno sensitive capabilityget_sectionsno sensitive capabilitylist_projectsno sensitive capabilitystatus_summaryno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (6 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v1.0.0 latest |
B 87/100 | 3 | 1.13.0 | 2026-09-07 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
npx mcptrustchecker scan @markfu909/overleaf-mcp --onlineOther implementations of Overleaf 5
Independent packages implementing the same tool, scanned with the same engine. Compare all 6 side by side →
MCP server for Overleaf projects
A minimal, auditable MCP server for editing Overleaf projects via Git
MCP server for Overleaf with tracked-changes support, via the Socket.IO web API (not the Git bridge).
MCP server connecting Claude to Overleaf — automated LaTeX resume editing, compilation, and job tailoring without copy-paste
MCP server that lets AI assistants read Overleaf projects, parse LaTeX document structure, and push section-level edits back via Git.
More in Data Science & ML
MCP server for 2s.io — 575+ pay-per-call tools for AI agents — ground-truth data, AI gateway, and agent infra (storage, locks, queues, watchers). x402 USDC on Base/Solana, no API keys, upto usage billing, free trials.
ArXiv preprints + Google Scholar papers, with citation counts in one query.
Add MCP servers to your favorite coding agents with a single command.
Help agents automatically write and test stories for your UI components
Model Context Protocol server for AI-Archive platform - enables AI agents to discover, submit, and review research papers
MCP server for Semantic Scholar research workflows with stdio and Streamable HTTP transports.