fastmcp-template 0.0.3

FastMCP server template with mcp-refcache and Langfuse tracing

FastMCP Template

A production-ready FastMCP server template with mcp-refcache integration for building AI agent tools that handle large data efficiently.

Using This Template

Create Your Project

1. Use as GitHub Template (recommended):

   • Click "Use this template" → "Create a new repository"
   • Or use GitHub CLI: gh repo create my-mcp-server --template l4b4r4b4b4/fastmcp-template

2. Or clone directly:

   git clone https://github.com/l4b4r4b4b4/fastmcp-template my-mcp-server
   cd my-mcp-server
   rm -rf .git && git init
   

Rename the Project

After creating your project, rename it from fastmcp-template to your project name:

1. Update pyproject.toml

[project]
name = "my-mcp-server"  # Your project name
version = "0.0.1"
description = "Your project description"
authors = [{ name = "Your Name", email = "[e-mail]" }]

[project.scripts]
my-mcp-server = "app.__main__:app"  # Your CLI command

2. Update Docker images in docker-compose.yml

services:
  my-mcp-server:
    image: ghcr.io/your-org/my-mcp-server:latest

3. Update docker/Dockerfile.base labels

LABEL org.opencontainers.image.source="https://github.com/your-org/my-mcp-server"

4. Update app/server.py

mcp = FastMCP(
    name="My MCP Server",  # Your server name
    instructions="Your server description...",
)

_cache = RefCache(
    name="my-mcp-server",  # Your cache namespace
    ...
)

5. Update GitHub workflows

In .github/workflows/release.yml:

env:
  APP_IMAGE_NAME: ${{ github.repository_owner }}/my-mcp-server
  BASE_IMAGE_NAME: ${{ github.repository_owner }}/my-mcp-base

6. Update Zed/Editor settings

In .zed/settings.json, update the context server name and command.

Add Your Tools

Create new tool modules in app/tools/:

# app/tools/my_feature.py
from pydantic import BaseModel, Field

class MyInput(BaseModel):
    """Input for my tool."""
    query: str = Field(description="The query to process")

async def my_tool(query: str) -> dict:
    """Process a query and return results."""
    # Your business logic here
    return {"result": f"Processed: {query}"}

Then register in app/tools/__init__.py and app/server.py:

# app/tools/__init__.py
from app.tools.my_feature import MyInput, my_tool
__all__ = [..., "MyInput", "my_tool"]

# app/server.py
from app.tools import my_tool
mcp.tool(my_tool)

Configure PyPI Publishing

1. Go to PyPI Trusted Publishers
2. Add a new pending publisher:
   • Project name: my-mcp-server
   • Owner: your-github-username
   • Repository: my-mcp-server
   • Workflow: publish.yml
   • Environment: pypi

Features

• Reference-Based Caching - Return references instead of large data, reducing context window usage
• Preview Generation - Automatic previews for large results (sample, truncate, paginate strategies)
• Pagination - Navigate large datasets without loading everything at once
• Access Control - Separate user and agent permissions for sensitive data
• Private Computation - Let agents compute with values they cannot see
• Docker Ready - Production-ready containers with Python slim base image
• Optional Langfuse Tracing - Built-in observability integration

Quick Start

Prerequisites

• Python 3.12+
• uv (recommended) or pip

Installation

# Clone the template
git clone https://github.com/l4b4r4b4b4/fastmcp-template
cd fastmcp-template

# Install dependencies
uv sync

# Run the server (stdio mode for Claude Desktop)
uv run fastmcp-template

# Run the server (SSE/HTTP mode for deployment)
uv run fastmcp-template --transport sse --port 8000

Install from PyPI

# Run directly with uvx (no install needed)
uvx fastmcp-template stdio

# Or install globally
uv tool install fastmcp-template
fastmcp-template --help

Docker Deployment

# Pull and run from GHCR
docker pull ghcr.io/l4b4r4b4b4/fastmcp-template:latest
docker run -p 8000:8000 ghcr.io/l4b4r4b4b4/fastmcp-template:latest

# Or build locally with Docker Compose
docker compose up

# Build images manually
docker compose --profile build build base
docker compose build

Using with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "fastmcp-template": {
      "command": "uv",
      "args": ["run", "fastmcp-template"],
      "cwd": "/path/to/fastmcp-template"
    }
  }
}

Using with Zed

The template includes .zed/settings.json pre-configured for MCP context servers.

Example Tools

The template includes several example tools demonstrating different patterns:

Simple Tool (No Caching)

@mcp.tool
def hello(name: str = "World") -> dict[str, Any]:
    """Say hello to someone."""
    return {"message": f"Hello, {name}!"}

Cached Tool (Public Namespace)

@mcp.tool
@cache.cached(namespace="public")
async def generate_items(count: int = 10, prefix: str = "item") -> list[dict]:
    """Generate items with automatic caching for large results."""
    return [{"id": i, "name": f"{prefix}_{i}"} for i in range(count)]

Private Computation (EXECUTE Permission)

@mcp.tool
def store_secret(name: str, value: float) -> dict[str, Any]:
    """Store a secret that agents can use but not read."""
    secret_policy = AccessPolicy(
        user_permissions=Permission.FULL,
        agent_permissions=Permission.EXECUTE,  # Can use, cannot see
    )
    ref = cache.set(key=f"secret_{name}", value=value, policy=secret_policy)
    return {"ref_id": ref.ref_id}

@mcp.tool
def compute_with_secret(secret_ref: str, multiplier: float = 1.0) -> dict[str, Any]:
    """Compute using a secret without revealing it."""
    secret = cache.resolve(secret_ref, actor=DefaultActor.system())
    return {"result": secret * multiplier}

Project Structure

fastmcp-template/
├── app/                     # Application code (flat structure for containers)
│   ├── __init__.py          # Version export
│   ├── server.py            # Main server with example tools
│   └── tools/               # Additional tool modules
├── docker/
│   ├── Dockerfile.base      # Python slim base image with dependencies
│   ├── Dockerfile           # Production image (extends base)
│   └── Dockerfile.dev       # Development with hot reload
├── tests/
│   ├── conftest.py          # Pytest fixtures
│   └── test_server.py       # Server tests
├── .github/
│   └── workflows/
│       ├── ci.yml           # CI pipeline (lint, test, security)
│       ├── publish.yml      # PyPI trusted publisher
│       └── release.yml      # Docker build & publish to GHCR
├── docker-compose.yml       # Local development & production
├── pyproject.toml           # Project config
├── flake.nix                # Nix dev shell
└── .rules                   # AI assistant guidelines

Development

Setup

# Install dependencies
uv sync

# Install pre-commit and pre-push hooks (configured in .pre-commit-config.yaml)
uv run pre-commit install --install-hooks
uv run pre-commit install --hook-type pre-push

Running Tests

uv run pytest
uv run pytest --cov  # With coverage

Linting and Formatting

uv run ruff check . --fix
uv run ruff format .

Type Checking

uv run mypy app/

Docker Development

# Run development container with hot reload
docker compose --profile dev up

# Build base image (for publishing)
docker compose --profile build build base

# Build all images
docker compose build

Using Nix (Optional)

nix develop  # Enter dev shell with all tools

Customization

1. Rename the project: Update pyproject.toml, app/, and imports
2. Add your tools: Create new tools in app/server.py or add modules to app/tools/
3. Configure caching: Adjust RefCache settings in app/server.py
4. Add Langfuse: Install with uv add langfuse and configure environment variables
5. Extend base image: Use FROM ghcr.io/l4b4r4b4b4/fastmcp-base:latest in your Dockerfile

Configuration

Environment Variables

Variable	Description	Default
LANGFUSE_PUBLIC_KEY	Langfuse public key	-
LANGFUSE_SECRET_KEY	Langfuse secret key	-
LANGFUSE_HOST	Langfuse host URL	https://cloud.langfuse.com

CLI Commands

uvx fastmcp-template --help

Commands:
  stdio             Start server in stdio mode (for Claude Desktop and local CLI)
  sse               Start server in SSE mode (Server-Sent Events)
  streamable-http   Start server in streamable HTTP mode (recommended for remote/Docker)

# Examples:
uvx fastmcp-template stdio                          # Local CLI mode
uvx fastmcp-template sse --port 8000                # SSE on port 8000
uvx fastmcp-template streamable-http --host 0.0.0.0 # Docker/remote mode

Test Prompts

Use these prompts in a fresh chat session to verify the MCP server is working correctly. Each prompt demonstrates different capabilities of mcp-refcache.

🟢 Basic: Hello World

Prompt: "Say hello to 'MCP Developer' using the fastmcp-template tools"

Expected: The assistant calls hello with name="MCP Developer" and returns a greeting.

---

🟢 Basic: Health Check

Prompt: "Check if the fastmcp-template server is healthy"

Expected: The assistant calls health_check and reports server status.

---

🟡 Intermediate: Generate & Explore Items

Prompt: "Generate 50 widgets and tell me about the first and last items"

Expected: The assistant calls generate_items(count=50, prefix="widget") and describes widget_0 and widget_49.

---

🟡 Intermediate: Salary Calculator (Private Computation)

Prompt: "I want to calculate a 5% raise on my salary, but I don't want you to know my actual salary. Store $75,000 as a secret called 'current_salary', then calculate what it would be with a 5% raise."

Expected:

1. Assistant calls store_secret(name="current_salary", value=75000)
2. Assistant calls compute_with_secret(secret_ref="...", multiplier=1.05)
3. Assistant reports the result ($78,750) without ever seeing the original value

---

🔴 Advanced: Multi-Step Private Computation

Prompt: "Help me compare two investment options without seeing my principal. Store $10,000 as 'principal'. Then calculate returns for: Option A (8% return) and Option B (12% return). Which is better?"

Expected:

1. Assistant stores the secret principal
2. Assistant computes both options using the same secret reference
3. Assistant compares results ($10,800 vs $11,200) and recommends Option B
4. The actual principal value is never revealed to the assistant

---

🔴 Advanced: Access Control Verification

Prompt: "Store my API key hash as a secret (use value 12345), then try to read it back directly using get_cached_result"

Expected:

1. Assistant stores the secret successfully
2. When attempting to read with get_cached_result, it gets "access denied"
3. Assistant explains that secrets have EXECUTE-only permission for agents

---

🔴 Advanced: Admin Tool Verification

Prompt: "Show me the cache statistics using admin_get_cache_stats"

Expected: Assistant receives "Admin access required" error and explains that admin tools are permission-gated.

---

Test Coverage Summary

Feature	Prompt Level	Tools Used
Basic greeting	🟢 Basic	hello
Server health	🟢 Basic	health_check
Item generation	🟡 Intermediate	generate_items
Secret storage	🟡 Intermediate	store_secret
Private computation	🟡 Intermediate	compute_with_secret
Access control	🔴 Advanced	get_cached_result
Admin gating	🔴 Advanced	admin_* tools

License

MIT License - see LICENSE for details.

Contributing

See CONTRIBUTING.md for development guidelines.

Related Projects

• mcp-refcache - Reference-based caching for MCP servers
• FastMCP - High-performance MCP server framework
• Model Context Protocol - The underlying protocol specification