FastMCP 3.0 Server Development
SkillCloud & infraComplete guide for building MCP servers with FastMCP 3.0 - tools, resources, authentication, providers, middleware, and deployment. Use when creating Python MCP servers or integrating AI models with external tools and data.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the FastMCP 3.0 Server Development skill
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
- Sep 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.
Advanced
- Item type
- skill
- Key
fastmcp-server- Source
- github.com/davila7/claude-code-templates
github.com/davila7/claude-code-templates