FastMCP is a Python framework for building Model Context Protocol applications, which connect language models with tools and data through servers and clients. Developers use it to expose Python functions as tools, connect to local or remote MCP servers, and build interactive tool interfaces. The catalogue includes eight skills, three instructions, two rules, one setting, and one hook for FastMCP.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PrefectHQ/fastmcp --skill fastmcp-client-cligit clone --depth 1 https://github.com/PrefectHQ/fastmcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/prefecthq/fastmcp/fastmcp-client-cli)<a href="https://agentmods.dev/skills/prefecthq/fastmcp/fastmcp-client-cli"><img src="https://agentmods.dev/badge/skills/prefecthq/fastmcp/fastmcp-client-cli/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/prefecthq/fastmcp/fastmcp-client-cli"><img src="https://agentmods.dev/badge/skills/prefecthq/fastmcp/fastmcp-client-cli.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 23 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 65 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00877 |
| Opus 5.5 | $0.00018 | $0.00351 |
| Sonnet 5.5 | $0.00009 | $0.00175 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
fastmcp-client-cli scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastMCP CLI: List and Call
Use fastmcp list and fastmcp call to interact with any MCP server from the command line.
Listing Tools
# Remote server
fastmcp list http://localhost:8000/mcp
# Local Python file (runs via fastmcp run automatically)
fastmcp list server.py
# MCPConfig with multiple servers
fastmcp list mcp.json
# Stdio command (npx, uvx, etc.)
fastmcp list --command 'npx -y @modelcontextprotocol/server-github'
# Include full input/output schemas
fastmcp list server.py --input-schema --output-schema
# Machine-readable JSON
fastmcp list server.py --json
# Include resources and prompts
fastmcp list server.py --resources --prompts
Default output shows tool signatures and descriptions. Use --input-schema or --output-schema to include full JSON schemas, --json for structured output.
Calling Tools
# Key=value arguments (auto-coerced to correct types)
fastmcp call server.py greet name=World
fastmcp call server.py add a=3 b=4
# Single JSON object for complex/nested args
fastmcp call server.py create_item '{"name": "Widget", "tags": ["a", "b"]}'
# --input-json with key=value overrides
fastmcp call server.py search --input-json '{"query": "hello", "limit": 5}' limit=10
# JSON output for scripting
fastmcp call server.py add a=3 b=4 --json
Type coercion is automatic: limit=5 becomes an integer, verbose=true becomes a boolean, based on the tool's input schema.
Server Targets
All commands accept the same server targets:
| Target | Example |
|---|---|
| HTTP/HTTPS URL | http://localhost:8000/mcp |
| Python file | server.py |
| MCPConfig JSON | mcp.json (must have mcpServers key) |
| Stdio command | --command 'npx -y @mcp/server' |
| Discovered name | weather or source:name |
Servers configured in editor configs (Claude Desktop, Claude Code, Cursor, Gemini CLI, Goose) or project-level mcp.json can be referenced by name. Use source:name (e.g. claude-code:my-server, cursor:weather) to target a specific source. Run fastmcp discover to see available names.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 115 lines · 46 tokens per session scan A d1984daf0f15
fastmcp-client-cli is a skill published in the GitHub repository PrefectHQ/fastmcp (28,150 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 877 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
first-reader
Beta readers for any draft, run by simulating how a real reader experiences it, moment by moment. A skim gate, a no-lookahead timed read producing an attention transcript with quit points, a recall test of what a reader remembers the next day, and a trust ledger of the implied author. Use when the user asks for a beta…
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV…
thinking-out-loud
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's…
hugging-face-datasets
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
autocontext-creator
Use when an agent needs to CREATE knowledge with Autocontext - run a scenario or plain-language task through the improvement loop, judge or improve a single output, and inspect what the run produced. Host-agnostic; requires only the autoctx CLI.
hugging-face-jobs
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads…