FastMCP 3.0 Server Development
SkillCloud & infrafastmcp-server is a skill that guides an agent through building MCP servers with FastMCP 3.0 in Python. It covers the full process of writing server code that connects AI models to external tools and data, from defining tools and resources to authentication, middleware, and deployment.
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Have a Python environment available for writing server code.
What your AI can do with it
- Guide creation of Python MCP servers using FastMCP 3.0
- Define tools and resources exposed to AI models
- Set up authentication and providers for a server
- Apply middleware in an MCP server
- Deploy a finished FastMCP server
- Integrate AI models with external tools and data
Getting started
- Have a Python environment available for writing server code.
- Add the fastmcp-server skill to the agent's available skills.
- Ask the agent to build an MCP server with FastMCP 3.0, describing the tools or data to connect.
- Follow the generated guidance through authentication, middleware, and deployment as needed.
What this skill tells your AI
The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/development/fastmcp-server/SKILL.md and read by ahel’s review.
Complete reference for building production-ready MCP (Model Context Protocol) servers with FastMCP 3.0 - the fast, Pythonic framework for connecting LLMs to tools and data.
When to use this skill
Use FastMCP Server when:
- Creating a new MCP server in Python
- Adding tools, resources, or prompts to an MCP server
- Implementing authentication (OAuth, OIDC, token verification)
- Setting up middleware for logging, rate limiting, or authorization
- Configuring providers (local, filesystem, skills, custom)
- Building production MCP servers with telemetry and storage
- Upgrading from FastMCP 2.x to 3.0
Key areas covered:
- Tools & Resources (CORE): Decorators, validation, return types, templates
- Context & DI (CORE): MCP context, dependency injection, background tasks
- Authentication (SECURITY): OAuth, OIDC, token verification, proxy patterns
- Authorization (SECURITY): Scope-based and role-based access control
- Middleware (ADVANCED): Request/response pipeline, built-in middleware
- Providers (ADVANCED): Local, filesystem, skills, and custom providers
- Features (ADVANCED): Pagination, sampling, storage, OpenTelemetry, versioning
Quick reference
Core patterns
Create a server with tools:
from fastmcp import FastMCP
mcp = FastMCP("MyServer")
@mcp.tool
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
Create a resource:
@mcp.resource("data://config")
def get_config() -> dict:
"""Return server configuration"""
return {"version": "1.0", "debug": False}
Create a resource template:
@mcp.resource("users://{user_id}/profile")
def get_user_profile(user_id: str) -> dict:
"""Get a user's profile by ID"""
return fetch_user(user_id)
Create a prompt:
@mcp.prompt
def review_code(code: str, language: str = "python") -> str:
"""Review code for best practices"""
return f"Review this {language} code:\n\n{code}"
Run the server:
if __name__ == "__main__":
mcp.run()
# Or with transport options:
# mcp.run(transport="sse", host="0.0.0.0", port=8000)
Using context in tools
from fastmcp import FastMCP, Context
mcp = FastMCP("MyServer")
@mcp.tool
def process_data(uri: str, ctx: Context) -> str:
"""Process data with logging and progress"""
ctx.info(f"Processing {uri}")
ctx.report_progress(0, 100)
data = ctx.read_resource(uri)
ctx.report_progress(100, 100)
return f"Processed: {data}"
Authentication setup
from fastmcp import FastMCP
from fastmcp.server.auth import BearerAuthProvider
auth = BearerAuthProvider(
jwks_uri="https://your-provider/.well-known/jwks.json",
audience="your-api",
issuer="https://your-provider/"
)
mcp = FastMCP("SecureServer", auth=auth)
Key concepts
Tools
Functions exposed as executable capabilities for LLMs. Decorated with @mcp.tool. Support Pydantic validation, async, custom return types, and annotations (readOnlyHint, destructiveHint).
Resources & Templates
Static or dynamic data sources identified by URIs. Resources use fixed URIs (data://config), templates use parameterized URIs (users://{id}/profile). Support MIME types, annotations, and wildcard parameters.
Context
The Context object provides access to MCP features within tools/resources: logging, progress reporting, resource access, LLM sampling, user elicitation, and session state.
Dependency Injection
Inject values into tool/resource functions using Depends(). Supports HTTP requests, access tokens, custom dependencies, and generator-based cleanup patterns.
Providers
Control where components come from. LocalProvider (default, decorator-based), FileSystemProvider (load from Python files on disk), SkillsProvider (packaged bundles), or custom providers.
Authentication & Authorization
Multiple auth patterns: token verification (JWT, JWKS), OAuth proxy, OIDC proxy, remote OAuth, and full OAuth server. Authorization via scopes on components and middleware.
Middleware
Intercept and modify requests/responses. Built-in middleware for rate limiting, error handling, logging, and response size limits. Custom middleware via @mcp.middleware.
Using the references
Detailed documentation is organized in the references/ folder:
Getting Started
- getting-started/installation.md - Install FastMCP, optional dependencies, verify setup
- getting-started/upgrade-guide.md - Migrate from FastMCP 2.x to 3.0
- getting-started/quickstart.md - First server, tools, resources, prompts, running
Server
- server/server-class.md - FastMCP server configuration, transport options, tag filtering
- server/tools.md - Tool decorator, parameters, validation, return types, annotations
- server/resources-and-templates.md - Resources, templates, URIs, wildcards, MIME types
Context
- context/mcp-context.md - Context object, logging, progress, resource access, sampling
- context/background-tasks.md - Long-running operations with task support
- context/dependency-injection.md - Depends(), custom deps, HTTP request, access tokens
- context/user-elicitation.md - Request structured input from users during execution
Features
- features/icons.md - Custom icons for tools, resources, prompts, and servers
- features/lifespans.md - Server lifecycle management and startup/shutdown hooks
- features/client-logging.md - Send log messages to MCP clients
- features/middleware.md - Request/response pipeline, built-in and custom middleware
- features/pagination.md - Paginate large component lists
- features/progress-reporting.md - Report progress for long-running operations
- features/sampling.md - Request LLM completions from the client
- features/storage-backends.md - Memory, file, and Redis storage for caching and tokens
- features/opentelemetry.md - Distributed tracing and observability
- features/versioning.md - Version components and filter by version ranges
Authentication
- authentication/token-verification.md - JWT, JWKS, introspection, static keys, custom
- authentication/remote-oauth.md - Delegate auth to upstream OAuth provider
- authentication/oauth-proxy.md - Full OAuth proxy with PKCE, client management
- authentication/oidc-proxy.md - OpenID Connect proxy with auto-discovery
- authentication/full-oauth-server.md - Complete built-in OAuth server
Authorization
- authorization.md - Scope-based access control, middleware authorization, patterns
Providers
- providers/local.md - Default provider, decorator-based component registration
- providers/filesystem.md - Load components from Python files on disk
- providers/skills.md - Package and distribute component bundles
- providers/custom.md - Build custom providers for any component source
Version history
v1.0.0 (February 2026)
- Initial release covering FastMCP 3.0 (release candidate)
- 30 reference files across 7 categories
- Complete coverage of tools, resources, context, auth, providers, and features
Signals
- GitHub stars
- 32k
- Forks
- 4k
- Last commit
- Oct 2026
ahel review
K1binfo
installs-packages (in references/context/background-tasks.md)K1binfo
installs-packages (in references/context/dependency-injection.md)K1binfo
installs-packages (in references/features/opentelemetry.md)K1binfo
installs-packages (in references/features/sampling.md)K1binfo
installs-packages (in references/features/storage-backends.md)K1binfo
installs-packages (in references/getting-started/installation.md)
Automated review, not a security audit. Ruleset v1+k2.
Questions
- What is fastmcp-server?
- A skill that guides an agent through building MCP servers with FastMCP 3.0 in Python, covering tools, resources, authentication, providers, middleware, and deployment.
- When should it be used?
- Use it when creating Python MCP servers or integrating AI models with external tools and data.
- Does it write the code itself?
- It guides the agent through writing Python server code; the agent produces the code following the skill's instructions.
- What does a finished server do?
- It connects AI models to external tools and data through the MCP protocol.
Advanced
- Item type
- skill
- Key
fastmcp-server- Source
- github.com/davila7/claude-code-templates
github.com/davila7/claude-code-templates
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