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FastMCP Server Review Capability Pack Skill

Expert FastMCP review skill for auditing Python MCP server structure, tools, resources, prompts, transports, and deployment readiness before release.

by oktofeesh1·added 2026-06-03·
Claude CodeCodexWindsurfGeminiCursorCLI
HarnessClaude CodeCodexWindsurfGeminiCursorCLI
Level:expertType:capability-packVerified:validated
Review first — review before installing

Open the source and read safety notes before installing.

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

Schema details

Install type
package
Reading time
9 min
Troubleshooting
Yes
Source repository stats
Scope
Source repo
Package metadata
Skill and platform metadata
Skill type
capability-pack
Skill level
expert
Verification
validated
Verified at
2026-06-03
Retrieval sources
https://gofastmcp.com/getting-started/welcomehttps://gofastmcp.com/servers/serverhttps://gofastmcp.com/servers/toolshttps://gofastmcp.com/servers/resourceshttps://gofastmcp.com/servers/promptshttps://gofastmcp.com/deployment/running-serverhttps://gofastmcp.com/patterns/clihttps://pypi.org/pypi/fastmcp/3.4.0/jsonhttps://raw.githubusercontent.com/PrefectHQ/fastmcp/v3.4.0/pyproject.tomlhttps://raw.githubusercontent.com/PrefectHQ/fastmcp/v3.4.0/README.md
Tested platforms
ClaudeCodexWindsurfGeminiCursorGeneric AGENTS
PlatformSupportInstall path
claude-codeNative.claude/skills/<skill-name>/SKILL.md
codexNative.agents/skills/<skill-name>/SKILL.md
windsurfNative.windsurf/skills/<skill-name>/SKILL.md
geminiNative.gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md
cursorAdapter.cursor/rules/<skill-name>.mdc
cliManualAGENTS.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.

Retrieval Sources

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

  1. Confirm the FastMCP package version, Python runtime, source tag, package source URL, and docs version used for review.
  2. Locate the FastMCP server object and inventory server name, instructions, mounts or imports, transport choices, and run path.
  3. 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.
  4. Inventory resources and templates. Check URI patterns, parameter handling, data source boundaries, missing-data behavior, caching assumptions, and response shape.
  5. Inventory prompts. Check argument names, default behavior, interpolation, wording stability, and whether prompt templates fit the intended client workflow.
  6. Review CLI and developer-run behavior from the FastMCP command path. Confirm entry command, module path, environment assumptions, log level, and expected transport.
  7. Review deployment readiness. Confirm process manager, target transport, health check or smoke check, dependency pinning, and rollback path.
  8. Build a validation plan using controlled sample inputs for critical tools, resources, prompts, launch, and failure cases.
  9. 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.

Output Contract

  1. Source evidence: FastMCP version, PyPI metadata, source tag, docs pages, and package constraints reviewed.
  2. Inventory: server object, entry command, tools, resources, prompts, transport, and deployment target.
  3. Findings: release blockers, non-blocking improvements, unknowns, and evidence links.
  4. Validation plan: exact launch, smoke, positive-path, negative-path, and regression checks.
  5. Release decision: accept, accept with caveats, request changes, or block release.
  6. 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.
#fastmcp#mcp#server-review#python#capability-pack

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