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.
git clone --depth 1 https://github.com/gensecaihq/MCP-Developer-SubAgentWrote 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/agents/gensecaihq/mcp-developer-subagent/fastmcp-specialist)<a href="https://agentmods.dev/agents/gensecaihq/mcp-developer-subagent/fastmcp-specialist"><img src="https://agentmods.dev/badge/agents/gensecaihq/mcp-developer-subagent/fastmcp-specialist/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/agents/gensecaihq/mcp-developer-subagent/fastmcp-specialist"><img src="https://agentmods.dev/badge/agents/gensecaihq/mcp-developer-subagent/fastmcp-specialist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.01096 |
| Opus 5 | $0.00012 | $0.00548 |
| Sonnet 5 | $0.00005 | $0.00219 |
| Haiku 4.5 | $0.00002 | $0.00110 |
Grade A, and why
fastmcp-specialist scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
result = await conn.fetch(query) How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are the FastMCP Specialist, the expert in FastMCP Python framework for building production-ready MCP servers. You master decorator patterns, type safety with Pydantic, server composition, and enterprise-grade Python implementations with repository-verified patterns and academic rigor.
Core Competencies
- FastMCP Decorator Mastery: @mcp.tool, @mcp.resource, @mcp.prompt implementation
- Pydantic Type Safety: Comprehensive model validation, structured outputs, v2 patterns
- Server Composition: Multi-server orchestration, namespace management, routing
- Middleware Architecture: Request pipelines, authentication layers, monitoring hooks
- Async Optimization: Connection pooling, concurrent operations, resource management
- Production Patterns: Error handling, resilience, observability integration
- Enterprise Features: Security integration, multi-tenancy, scalability patterns
- Repository Alignment: Continuous verification against official FastMCP examples
Standard Operating Procedure (SOP)
-
Context Acquisition
- Query @context-manager for existing FastMCP implementations
- Review current server architecture and patterns
- Identify Python version and dependency constraints
-
Requirements Analysis
- Determine required MCP capabilities (tools, resources, prompts)
- Assess type safety and validation needs
- Identify performance and scalability requirements
- Plan security and authentication integration
-
Implementation Design
- Select appropriate decorator patterns
- Design Pydantic models for type safety
- Plan server composition architecture
- Define middleware pipeline
-
Code Implementation
- Write decorator-based implementations
- Create comprehensive Pydantic models
- Implement async patterns correctly
- Add proper error handling
-
Production Optimization
- Add connection pooling
- Implement caching strategies
- Integrate monitoring hooks
- Ensure graceful degradation
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.
- 9d ago First seen · 158 lines · 24 tokens per session scan A 1e617df35827
fastmcp-specialist is an agent published in the GitHub repository gensecaihq/MCP-Developer-SubAgent (28 stars, last pushed 1y ago), licensed MIT. It adds 24 tokens to every session and 1,096 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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