Installing FastMCP adds Python packages to the selected environment; use an isolated project environment and pin the reviewed version., Reviewing a server can exercise tool, resource, and prompt paths; use controlled fixtures and avoid live side effects unless the release plan explicitly permits them., The source ZIP is external and version-pinned for reference; package trust should remain a maintainer decision., Treat destructive tools, write-capable resources, network calls, and long-running jobs as release-blocking until their behavior is documented and tested.
Privacy notes
FastMCP tools and resources can expose files, API responses, database records, prompts, and application state., Keep review notes focused on public source links, object names, version data, and summarized findings; omit operational details that do not need to be public.
4 safety and 2 privacy notes across 4 risk areas. Review closely: network access.
4 areas
SafetyExecution & processesInstalling FastMCP adds Python packages to the selected environment; use an isolated project environment and pin the reviewed version.
SafetyLocal filesReviewing a server can exercise tool, resource, and prompt paths; use controlled fixtures and avoid live side effects unless the release plan explicitly permits them.
SafetyGeneralThe source ZIP is external and version-pinned for reference; package trust should remain a maintainer decision.
SafetyNetwork accessTreat destructive tools, write-capable resources, network calls, and long-running jobs as release-blocking until their behavior is documented and tested.
PrivacyLocal filesFastMCP tools and resources can expose files, API responses, database records, prompts, and application state.
PrivacyGeneralKeep review notes focused on public source links, object names, version data, and summarized findings; omit operational details that do not need to be public.
Safety notes
Installing FastMCP adds Python packages to the selected environment; use an isolated project environment and pin the reviewed version.
Reviewing a server can exercise tool, resource, and prompt paths; use controlled fixtures and avoid live side effects unless the release plan explicitly permits them.
The source ZIP is external and version-pinned for reference; package trust should remain a maintainer decision.
Treat destructive tools, write-capable resources, network calls, and long-running jobs as release-blocking until their behavior is documented and tested.
Privacy notes
FastMCP tools and resources can expose files, API responses, database records, prompts, and application state.
Keep review notes focused on public source links, object names, version data, and summarized findings; omit operational details that do not need to be public.
Prerequisites
FastMCP server implementation, pull request, or design draft
Python environment compatible with FastMCP 3.4.0 and Python 3.10 or newer
Tool, resource, prompt, transport, and deployment target inventory
Expected client behavior and sample inputs for critical tools
Approval to inspect the server code, configuration, and review fixtures
.gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md
cursor
Adapter
.cursor/rules/<skill-name>.mdc
cli
Manual
AGENTS.md or tool-specific context file
Full copyable content
# Trigger
"Apply the FastMCP server review capability pack to this MCP server."
# Required output
1) FastMCP package/source version and Python runtime check
2) Server, tool, resource, prompt, and CLI inventory
3) Review findings with release-blocking and non-blocking issues
4) Validation plan and final release recommendation
About this resource
Knowledge Freshness
This capability pack is pinned to FastMCP 3.4.0 documentation, PyPI metadata, and source files verified on 2026-06-03. The reviewed package metadata requires Python 3.10 or newer.
Prefer the current FastMCP docs, PyPI metadata, and pinned source files over model memory for CLI behavior, package version, server APIs, and deployment notes.
Scope Note
This is not a generic MCP security checklist. Use it when the server under review is implemented with FastMCP and the review needs framework-aware coverage of server construction, tools, resources, prompts, runtime invocation, and release evidence.
Core Workflow
Confirm the FastMCP package version, Python runtime, source tag, package source URL, and docs version used for review.
Locate the FastMCP server object and inventory server name, instructions, mounts or imports, transport choices, and run path.
Inventory tools defined through FastMCP decorators or registration helpers. Check function names, docstrings, type hints, validation expectations, return shape, side effects, timeout behavior, and diagnostics.
Inventory resources and templates. Check URI patterns, parameter handling, data source boundaries, missing-data behavior, caching assumptions, and response shape.
Inventory prompts. Check argument names, default behavior, interpolation, wording stability, and whether prompt templates fit the intended client workflow.
Review CLI and developer-run behavior from the FastMCP command path. Confirm entry command, module path, environment assumptions, log level, and expected transport.
Review deployment readiness. Confirm process manager, target transport, health check or smoke check, dependency pinning, and rollback path.
Build a validation plan using controlled sample inputs for critical tools, resources, prompts, launch, and failure cases.
Produce a release recommendation with blockers, non-blocking improvements, evidence links, and exact checks that must pass before exposing the server.
Capability Scope
FastMCP package/source verification
Server object and runtime path review
Tool inventory and behavior review
Resource and resource-template review
Prompt-template review
CLI invocation and local run checks
Deployment readiness and rollback planning
Evidence-based release decision for FastMCP servers
Compatibility
Native
Claude Code / Claude: use as a reusable Agent Skill for FastMCP server PR and release review.
Codex/OpenAI workflows: use as SKILL.md-style instructions for FastMCP code review and release planning.
Manual Adaptation
Windsurf and Gemini: adapt the workflow and output contract into their skill formats.
Cursor and Generic AGENTS files: convert the review rules and validation checklist into repository-level MCP review rules.
Required Inputs
FastMCP package version and Python version
Server entrypoint, module path, and entry command
Tool, resource, and prompt inventory
Intended clients and transport mode
Deployment target and rollback approach
Sample inputs and expected outputs for critical paths
Production Rules
Do not approve a server until the FastMCP version, Python version, entrypoint, and transport mode are explicit.
Do not treat generated schemas as sufficient review evidence; check the underlying Python function behavior and side effects.
Mark tools as release-blocking when they mutate state, call external systems, or run long jobs without clear limits and tests.
Mark resources as release-blocking when their URI templates or data boundaries are ambiguous.
Mark prompts as release-blocking when required arguments, defaults, or output expectations are unclear.
Keep deployment review separate from local demo success; a server that runs locally can still be unready for shared use.
Prefer pinned package versions and reproducible entry commands over latest-version examples.
Inventory: server object, entry command, tools, resources, prompts, transport, and deployment target.
Findings: release blockers, non-blocking improvements, unknowns, and evidence links.
Validation plan: exact launch, smoke, positive-path, negative-path, and regression checks.
Release decision: accept, accept with caveats, request changes, or block release.
Follow-up: owner, file path, or source area for each required change.
Troubleshooting
Issue: The server starts locally but clients cannot discover tools
Fix: Check the FastMCP server entrypoint, selected transport, CLI command, tool registration path, and whether the reviewed process is the same process the client launches.
Issue: Tool schemas look correct but runtime behavior is unstable
Fix: Review the Python function body, type hints, default values, error handling, and sample inputs instead of relying only on generated schema output.
Issue: Resources return inconsistent shapes
Fix: Add explicit response contracts for each resource URI or template, then test empty, missing, large, and malformed input cases.
Issue: Prompt templates drift across releases
Fix: Pin prompt argument names and expected output roles in review notes, then add regression examples for the prompts that clients depend on.
Issue: Deployment differs from local development
Fix: Compare the local command, deployed command, package version, Python version, transport, and runtime settings before approving release.
Validation Checklist
FastMCP version and Python runtime verified.
Source tag, PyPI metadata, docs, and package URL checked.
Server entrypoint and entry command confirmed.
Tool, resource, and prompt inventory completed.
Critical tools tested with positive, negative, and boundary inputs.
Resource templates tested for valid, missing, and malformed parameters.
Prompt templates checked for argument stability and expected output shape.
Transport and deployment path smoke-tested.
Rollback or disable path documented for release-blocking failures.
Show that FastMCP Server Review Capability Pack Skill is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/skills/fastmcp-server-review-capability-pack)
How it compares
FastMCP Server Review Capability Pack Skill side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
✓Installing FastMCP adds Python packages to the selected environment; use an isolated project environment and pin the reviewed version.
Reviewing a server can exercise tool, resource, and prompt paths; use controlled fixtures and avoid live side effects unless the release plan explicitly permits them.
The source ZIP is external and version-pinned for reference; package trust should remain a maintainer decision.
Treat destructive tools, write-capable resources, network calls, and long-running jobs as release-blocking until their behavior is documented and tested.
✓Installing Spectral adds npm packages to the selected project environment; pin the reviewed version and avoid global installs for review work.
Spectral can lint local files, globs, and remote contract URLs; review source locations before running checks in shared CI.
Ruleset changes can silently weaken future API gates; review disabled rules, severity changes, and overrides as release-impacting changes.
JavaScript rulesets and custom functions such as `.spectral.js` execute Node.js code; do not run attacker-supplied rulesets from untrusted PRs unless they have been inspected and executed in a sandboxed environment.
The source ZIP is external and version-pinned for reference; package trust should remain a maintainer decision.
✓Installing `llms-txt` adds Python dependencies in the selected environment; pin the reviewed version and prefer project-scoped tooling.
The `llms_txt2ctx` helper can retrieve linked HTTPS resources while expanding context; review target URLs before running it against private staging content.
Search artifacts influence crawler and model-facing discovery signals; treat canonical, robots, sitemap, and structured data changes as publish-impacting.
The source archive is external and version-pinned for reference; package trust should remain a maintainer decision.
✓Installing reg-suit adds npm packages to the selected project environment; pin the reviewed package version and avoid global installs for review work.
reg-suit can publish image snapshots and HTML reports through storage plugins such as S3 or Google Cloud Storage; review destination configuration before running in shared CI.
Visual baselines can normalize accidental UI changes if accepted too casually; require owner approval for broad layout, color, text, or viewport changes.
The source ZIP is external and version-pinned for reference; package trust should remain a maintainer decision.
Privacy notes
✓FastMCP tools and resources can expose files, API responses, database records, prompts, and application state.
Keep review notes focused on public source links, object names, version data, and summarized findings; omit operational details that do not need to be public.
✓OpenAPI files can reveal internal route names, hostnames, example payloads, business object names, and planned endpoints.
Lint reports can include source paths, schema paths, rule names, snippets, and examples from the reviewed contract.
Keep public review notes focused on rule IDs, contract paths, compatibility impact, and summarized examples; omit details that do not need to be public.
✓`/llms.txt`, expanded context, sitemap, and structured data artifacts can expose URL paths, page titles, product terms, doc structure, and internal naming.
Context expansion can collect linked markdown content into a single artifact, which may reveal more detail than the compact index.
Keep public review notes focused on artifact shape, URL classes, stale links, and source evidence; avoid exposing private staging paths or unreleased content.
✓Rendered UI images and reports can expose environment URLs, branch names, visible UI text, test account data, and product screenshots.
Storage and notification plugins can publish report links to CI systems, pull request comments, chat tools, or cloud buckets.
Keep public review notes focused on changed components, thresholds, artifact links, and summarized findings; omit sensitive rendered content that does not need to be public.
Prerequisites
FastMCP server implementation, pull request, or design draft
Python environment compatible with FastMCP 3.4.0 and Python 3.10 or newer
Tool, resource, prompt, transport, and deployment target inventory
Expected client behavior and sample inputs for critical tools
OpenAPI v2, v3.0, or v3.1 contract file set
Spectral ruleset such as `.spectral.yaml` or an explicit ruleset path
Lint output, CI output, or proposed contract diff
API owner expectations for breaking changes, naming, examples, and release gates
Published or staged documentation site with a known canonical origin
`/llms.txt` draft, `llms-full.txt` or expanded context artifact when available
`sitemap.xml`, `robots.txt`, canonical URL policy, and structured data inventory
Python 3.10 or newer when using the pinned `llms-txt` parser and context helpers
UI change, pull request, or release candidate with rendered image artifacts
reg-suit configuration such as `regconfig.json`
Baseline image source, actual image directory, and generated report location
Threshold policy for accepted pixel or rate differences