F

Public audit · 2026-04-30

punkpeye/fastmcp

Overall: F (35/100) · v0.3 scan · 6 axes · LLM prompt-injection probe

SkillAudit report — punkpeye/fastmcp

Scanned 2026-04-30 by SkillAudit v0.3 (surface-tiered static checks + LLM-assisted prompt-injection red-team).
Commit: 72c1166 · Stars: 3055 · Days since last push: 4
LLM prompt-injection probe: no-tool-surface

Overall grade: F (35/100)

AxisScoreGrade
security35/100F
permissions100/100A
credentials95/100A
maintenance100/100A
compatibility70/100C⚠️
docs90/100A

Security findings

Production sources:

const res = await fetch(url);

const response = await fetch(input.url);

const response = await fetch(input.url);

Test source (low-weight) — 3 total, deduct 5/0 per high/warn:

const response = await fetch(\http://localhost:${port}/ping\);

const dcrResponse = await fetch(

const metadataResponse = await fetch(

Permissions

_No findings on this axis._

Credentials

Examples / samples (low-weight) — 1 total, deduct 5/0 per high/warn:

console.log(\`

Maintenance

_No findings on this axis._

Compatibility

Production sources:

Documentation

Production sources:

missing


Methodology

SkillAudit v0.3 clones the repo at the provided ref (default: default branch, HEAD) into an ephemeral sandbox, runs six static checks over .js/.ts/.py sources, queries the GitHub API for maintenance signals, and runs an LLM-assisted prompt-injection red-team over the MCP tool surface. Each axis is scored against the published rubric — surface tiers, per-(axis, surface) caps, grade buckets, and worked examples are all documented there.

The v0.3 calibration update introduces surface tiering: every finding is tagged with the code path it lives in (production / installer / examples / benchmarks / scripts / test). Production findings deduct at full weight (-30 high, -10 warn); installer findings deduct at half (-15 / -5); examples, benchmarks, top-level scripts, and tests deduct at low weight (-5 / 0). This stops a chatty benchmarks/ or samples/ directory from dominating an otherwise-clean MCP server's grade.

The prompt-injection axis extracts each server.tool(...) / @app.tool registration + the first ~60 lines of handler body, hands them to Claude Haiku 4.5 with a red-team system prompt, and asks for structured findings on untrusted-content flow into tool responses. One API call per scan, bounded at ~15K input tokens.

How to improve this grade

_Report generated by skillaudit.dev_

Want your repo audited?

First 100 audits go to waitlist signups in order. The engine runs against public GitHub URLs today.

Join the waitlist →