hermes-agent/optional-skills/mcp/fastmcp/SKILL.md
Teknium db22efbe88 feat(optional-skills): declare platforms frontmatter for all 63 undeclared skills
Extends the Windows-gating work to the optional-skills/ tree. Every
SKILL.md that previously omitted the platforms: field now carries an
explicit declaration, which Hermes's loader (agent.skill_utils.
skill_matches_platform) honors to skip-load on incompatible OSes.

58 skills declared cross-platform (platforms: [linux, macos, windows]):
  autonomous-ai-agents/blackbox, autonomous-ai-agents/honcho
  blockchain/base, blockchain/solana
  communication/one-three-one-rule
  creative/blender-mcp, creative/concept-diagrams, creative/hyperframes,
  creative/kanban-video-orchestrator, creative/meme-generation
  devops/cli (inference-sh-cli), devops/docker-management
  dogfood/adversarial-ux-test
  email/agentmail
  finance/3-statement-model, finance/comps-analysis, finance/dcf-model,
  finance/excel-author, finance/lbo-model, finance/merger-model,
  finance/pptx-author
  health/fitness-nutrition, health/neuroskill-bci
  mcp/fastmcp, mcp/mcporter
  migration/openclaw-migration
  mlops/accelerate, mlops/chroma, mlops/clip, mlops/guidance,
  mlops/hermes-atropos-environments, mlops/huggingface-tokenizers,
  mlops/instructor, mlops/lambda-labs, mlops/llava, mlops/modal,
  mlops/peft, mlops/pinecone, mlops/pytorch-lightning, mlops/qdrant,
  mlops/saelens, mlops/simpo, mlops/stable-diffusion
  productivity/canvas, productivity/shop-app, productivity/shopify,
  productivity/siyuan, productivity/telephony
  research/domain-intel, research/drug-discovery, research/duckduckgo-search,
  research/gitnexus-explorer, research/parallel-cli, research/scrapling
  security/1password, security/oss-forensics, security/sherlock
  web-development/page-agent

5 skills gated from Windows (platforms: [linux, macos]):
  mlops/flash-attention   - Flash Attention wheels are Linux-first; Windows
                            install requires building from source with CUDA
  mlops/faiss             - faiss-gpu has no Windows wheel; gate rather than
                            leak partial (faiss-cpu) support
  mlops/nemo-curator      - NVIDIA NeMo ecosystem has no first-class Windows path
  mlops/slime             - Megatron+SGLang RL stack is Linux-only in practice
  mlops/whisper           - openai-whisper + ffmpeg setup on Windows is
                            non-trivial; gate until Windows install stanza lands

Methodology: scanned every SKILL.md for Windows-hostile signals
(apt-get, brew, systemd, osascript, ptrace, X11 binaries, POSIX-only
Python APIs, Docker POSIX $(pwd) bind-mounts, explicit 'linux-only' /
'macos-only' text). 3 skills flagged as having hard signals on review:
docker-management and qdrant only had POSIX $(pwd) docker examples and
the tools themselves (Docker Desktop, Qdrant) run fine on Windows —
declared ALL. whisper had an apt/brew ffmpeg install path and nothing
else but the openai-whisper Windows install story is rough enough to
warrant gating.

Strict-over-lenient policy: when in doubt, gate. Easier to un-gate after
verified Windows support lands than to leak partial support that
manifests as mid-task failures for Windows users.
2026-05-08 14:27:40 -07:00

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Markdown

---
name: fastmcp
description: Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.
version: 1.0.0
author: Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
hermes:
tags: [MCP, FastMCP, Python, Tools, Resources, Prompts, Deployment]
homepage: https://gofastmcp.com
related_skills: [native-mcp, mcporter]
prerequisites:
commands: [python3]
---
# FastMCP
Build MCP servers in Python with FastMCP, validate them locally, install them into MCP clients, and deploy them as HTTP endpoints.
## When to Use
Use this skill when the task is to:
- create a new MCP server in Python
- wrap an API, database, CLI, or file-processing workflow as MCP tools
- expose resources or prompts in addition to tools
- smoke-test a server with the FastMCP CLI before wiring it into Hermes or another client
- install a server into Claude Code, Claude Desktop, Cursor, or a similar MCP client
- prepare a FastMCP server repo for HTTP deployment
Use `native-mcp` when the server already exists and only needs to be connected to Hermes. Use `mcporter` when the goal is ad-hoc CLI access to an existing MCP server instead of building one.
## Prerequisites
Install FastMCP in the working environment first:
```bash
pip install fastmcp
fastmcp version
```
For the API template, install `httpx` if it is not already present:
```bash
pip install httpx
```
## Included Files
### Templates
- `templates/api_wrapper.py` - REST API wrapper with auth header support
- `templates/database_server.py` - read-only SQLite query server
- `templates/file_processor.py` - text-file inspection and search server
### Scripts
- `scripts/scaffold_fastmcp.py` - copy a starter template and replace the server name placeholder
### References
- `references/fastmcp-cli.md` - FastMCP CLI workflow, installation targets, and deployment checks
## Workflow
### 1. Pick the Smallest Viable Server Shape
Choose the narrowest useful surface area first:
- API wrapper: start with 1-3 high-value endpoints, not the whole API
- database server: expose read-only introspection and a constrained query path
- file processor: expose deterministic operations with explicit path arguments
- prompts/resources: add only when the client needs reusable prompt templates or discoverable documents
Prefer a thin server with good names, docstrings, and schemas over a large server with vague tools.
### 2. Scaffold from a Template
Copy a template directly or use the scaffold helper:
```bash
python ~/.hermes/skills/mcp/fastmcp/scripts/scaffold_fastmcp.py \
--template api_wrapper \
--name "Acme API" \
--output ./acme_server.py
```
Available templates:
```bash
python ~/.hermes/skills/mcp/fastmcp/scripts/scaffold_fastmcp.py --list
```
If copying manually, replace `__SERVER_NAME__` with a real server name.
### 3. Implement Tools First
Start with `@mcp.tool` functions before adding resources or prompts.
Rules for tool design:
- Give every tool a concrete verb-based name
- Write docstrings as user-facing tool descriptions
- Keep parameters explicit and typed
- Return structured JSON-safe data where possible
- Validate unsafe inputs early
- Prefer read-only behavior by default for first versions
Good tool examples:
- `get_customer`
- `search_tickets`
- `describe_table`
- `summarize_text_file`
Weak tool examples:
- `run`
- `process`
- `do_thing`
### 4. Add Resources and Prompts Only When They Help
Add `@mcp.resource` when the client benefits from fetching stable read-only content such as schemas, policy docs, or generated reports.
Add `@mcp.prompt` when the server should provide a reusable prompt template for a known workflow.
Do not turn every document into a prompt. Prefer:
- tools for actions
- resources for data/document retrieval
- prompts for reusable LLM instructions
### 5. Test the Server Before Integrating It Anywhere
Use the FastMCP CLI for local validation:
```bash
fastmcp inspect acme_server.py:mcp
fastmcp list acme_server.py --json
fastmcp call acme_server.py search_resources query=router limit=5 --json
```
For fast iterative debugging, run the server locally:
```bash
fastmcp run acme_server.py:mcp
```
To test HTTP transport locally:
```bash
fastmcp run acme_server.py:mcp --transport http --host 127.0.0.1 --port 8000
fastmcp list http://127.0.0.1:8000/mcp --json
fastmcp call http://127.0.0.1:8000/mcp search_resources query=router --json
```
Always run at least one real `fastmcp call` against each new tool before claiming the server works.
### 6. Install into a Client When Local Validation Passes
FastMCP can register the server with supported MCP clients:
```bash
fastmcp install claude-code acme_server.py
fastmcp install claude-desktop acme_server.py
fastmcp install cursor acme_server.py -e .
```
Use `fastmcp discover` to inspect named MCP servers already configured on the machine.
When the goal is Hermes integration, either:
- configure the server in `~/.hermes/config.yaml` using the `native-mcp` skill, or
- keep using FastMCP CLI commands during development until the interface stabilizes
### 7. Deploy After the Local Contract Is Stable
For managed hosting, Prefect Horizon is the path FastMCP documents most directly. Before deployment:
```bash
fastmcp inspect acme_server.py:mcp
```
Make sure the repo contains:
- a Python file with the FastMCP server object
- `requirements.txt` or `pyproject.toml`
- any environment-variable documentation needed for deployment
For generic HTTP hosting, validate the HTTP transport locally first, then deploy on any Python-compatible platform that can expose the server port.
## Common Patterns
### API Wrapper Pattern
Use when exposing a REST or HTTP API as MCP tools.
Recommended first slice:
- one read path
- one list/search path
- optional health check
Implementation notes:
- keep auth in environment variables, not hardcoded
- centralize request logic in one helper
- surface API errors with concise context
- normalize inconsistent upstream payloads before returning them
Start from `templates/api_wrapper.py`.
### Database Pattern
Use when exposing safe query and inspection capabilities.
Recommended first slice:
- `list_tables`
- `describe_table`
- one constrained read query tool
Implementation notes:
- default to read-only DB access
- reject non-`SELECT` SQL in early versions
- limit row counts
- return rows plus column names
Start from `templates/database_server.py`.
### File Processor Pattern
Use when the server needs to inspect or transform files on demand.
Recommended first slice:
- summarize file contents
- search within files
- extract deterministic metadata
Implementation notes:
- accept explicit file paths
- check for missing files and encoding failures
- cap previews and result counts
- avoid shelling out unless a specific external tool is required
Start from `templates/file_processor.py`.
## Quality Bar
Before handing off a FastMCP server, verify all of the following:
- server imports cleanly
- `fastmcp inspect <file.py:mcp>` succeeds
- `fastmcp list <server spec> --json` succeeds
- every new tool has at least one real `fastmcp call`
- environment variables are documented
- the tool surface is small enough to understand without guesswork
## Troubleshooting
### FastMCP command missing
Install the package in the active environment:
```bash
pip install fastmcp
fastmcp version
```
### `fastmcp inspect` fails
Check that:
- the file imports without side effects that crash
- the FastMCP instance is named correctly in `<file.py:object>`
- optional dependencies from the template are installed
### Tool works in Python but not through CLI
Run:
```bash
fastmcp list server.py --json
fastmcp call server.py your_tool_name --json
```
This usually exposes naming mismatches, missing required arguments, or non-serializable return values.
### Hermes cannot see the deployed server
The server-building part may be correct while the Hermes config is not. Load the `native-mcp` skill and configure the server in `~/.hermes/config.yaml`, then restart Hermes.
## References
For CLI details, install targets, and deployment checks, read `references/fastmcp-cli.md`.