Project Configuration

import { VersionBadge } from "/snippets/version-badge.mdx"

FastMCP supports declarative configuration through fastmcp.json files. This is the canonical and preferred way to configure FastMCP projects, providing a single source of truth for server settings, dependencies, and deployment options that replaces complex command-line arguments.

The fastmcp.json file is designed to be a portable description of your server configuration that can be shared across environments and teams. When running from a fastmcp.json file, you can override any configuration values using CLI arguments.

Overview

The fastmcp.json configuration file allows you to define all aspects of your FastMCP server in a structured, shareable format. Instead of remembering command-line arguments or writing shell scripts, you declare your server's configuration once and use it everywhere.

When you have a fastmcp.json file, running your server becomes as simple as:

# Run the server using the configuration
fastmcp run fastmcp.json

# Or if fastmcp.json exists in the current directory
fastmcp run

This configuration approach ensures reproducible deployments across different environments, from local development to production servers. It works seamlessly with Claude Desktop, VS Code extensions, and any MCP-compatible client.

File Structure

The fastmcp.json configuration answers three fundamental questions about your server:

• Source = WHERE does your server code live?
• Environment = WHAT environment setup does it require?
• Deployment = HOW should the server run?

This conceptual model helps you understand the purpose of each configuration section and organize your settings effectively. The configuration file maps directly to these three concerns:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    // WHERE: Location of your server code
    "type": "filesystem",  // Optional, defaults to "filesystem"
    "path": "server.py",
    "entrypoint": "mcp"
  },
  "environment": {
    // WHAT: Environment setup and dependencies
    "type": "uv",  // Optional, defaults to "uv"
    "python": ">=3.10",
    "dependencies": ["pandas", "numpy"]
  },
  "deployment": {
    // HOW: Runtime configuration
    "transport": "stdio",
    "log_level": "INFO"
  }
}

Only the source field is required. The environment and deployment sections are optional and provide additional configuration when needed.

JSON Schema Support

FastMCP provides JSON schemas for IDE autocomplete and validation. Add the schema reference to your fastmcp.json for enhanced developer experience:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "path": "server.py",
    "entrypoint": "mcp"
  }
}

Two schema URLs are available:

• Version-specific: https://gofastmcp.com/public/schemas/fastmcp.json/v1.json
• Latest version: https://gofastmcp.com/public/schemas/fastmcp.json/latest.json

Modern IDEs like VS Code will automatically provide autocomplete suggestions, validation, and inline documentation when the schema is specified.

Source Configuration

The source configuration determines WHERE your server code lives. It tells FastMCP how to find and load your server, whether it's a local Python file, a remote repository, or hosted in the cloud. This section is required and forms the foundation of your configuration.

<ParamField body="path" type="string" required>
  Path to the Python file containing your FastMCP server. 
</ParamField>

<ParamField body="entrypoint" type="string">
  Name of the server instance or factory function within the module:
  - Can be a FastMCP server instance (e.g., `mcp = FastMCP("MyServer")`)
  - Can be a function with no arguments that returns a FastMCP server
  - If not specified, FastMCP searches for common names: `mcp`, `server`, or `app`
</ParamField>

**Example:**
```json
"source": {
  "type": "filesystem",
  "path": "src/server.py",
  "entrypoint": "mcp"
}
```

Note: File paths are resolved relative to the configuration file's location.

Future releases will support additional source types:

• Git repositories (type: "git") for loading server code directly from version control
• Prefect Horizon (type: "cloud") for hosted servers with automatic scaling and management </Note>

Environment Configuration

The environment configuration determines WHAT environment setup your server requires. It controls the build-time setup of your Python environment, ensuring your server runs with the exact Python version and dependencies it requires. This section creates isolated, reproducible environments across different systems.

FastMCP uses an extensible environment system with a base Environment class that can be implemented by different environment providers. Currently, FastMCP supports the UVEnvironment for Python environment management using uv's powerful dependency resolver.

<ParamField body="python" type="string">
  Python version constraint. Examples:
  - Exact version: `"3.12"`
  - Minimum version: `">=3.10"`
  - Version range: `">=3.10,<3.13"`
</ParamField>

<ParamField body="dependencies" type="list[str]">
  List of pip packages with optional version specifiers (PEP 508 format).
  ```json
  "dependencies": ["pandas>=2.0", "requests", "httpx"]
  ```
</ParamField>

<ParamField body="requirements" type="string">
  Path to a requirements.txt file, resolved relative to the config file location.
  ```json
  "requirements": "requirements.txt"
  ```
</ParamField>

<ParamField body="project" type="string">
  Path to a project directory containing pyproject.toml for uv project management.
  ```json
  "project": "."
  ```
</ParamField>

<ParamField body="editable" type="list[string]">
  List of paths to packages to install in editable/development mode. Useful for local development when you want changes to be reflected immediately. Supports multiple packages for monorepo setups or shared libraries.
  ```json
  "editable": ["."]
  ```
  Or with multiple packages:
  ```json
  "editable": [".", "../shared-lib", "/path/to/another-package"]
  ```
</ParamField>

**Example:**
```json
"environment": {
  "type": "uv",
  "python": ">=3.10",
  "dependencies": ["pandas", "numpy"],
  "editable": ["."]
}
```

Note: When any UVEnvironment field is specified, FastMCP automatically creates an isolated environment using `uv` before running your server.

When environment configuration is provided, FastMCP:

1. Detects the environment type (defaults to "uv" if not specified)
2. Creates an isolated environment using the appropriate provider
3. Installs the specified dependencies
4. Runs your server in this clean environment

This build-time setup ensures your server always has the dependencies it needs, without polluting your system Python or conflicting with other projects.

Similar to source types, future releases may support additional environment types for different runtime requirements, such as Docker containers or language-specific environments beyond Python. </Note>

Deployment Configuration

The deployment configuration controls HOW your server runs. It defines the runtime behavior including network settings, environment variables, and execution context. These settings determine how your server operates when it executes, from transport protocols to logging levels.

Environment variables are included in this section because they're runtime configuration that affects how your server behaves when it executes, not how its environment is built. The deployment configuration is applied every time your server starts, controlling its operational characteristics.

<ParamField body="host" type="string" default="127.0.0.1">
  Network interface to bind (HTTP transport only):
  - `"127.0.0.1"`: Local connections only
  - `"0.0.0.0"`: All network interfaces
</ParamField>

<ParamField body="port" type="integer" default="3000">
  Port number for HTTP transport.
</ParamField>

<ParamField body="path" type="string" default="/mcp/">
  URL path for the MCP endpoint when using HTTP transport.
</ParamField>

<ParamField body="log_level" type="string" default="INFO">
  Server logging verbosity. Options:
  - `"DEBUG"`: Detailed debugging information
  - `"INFO"`: General informational messages
  - `"WARNING"`: Warning messages
  - `"ERROR"`: Error messages only
  - `"CRITICAL"`: Critical errors only
</ParamField>

<ParamField body="env" type="object">
  Environment variables to set when running the server. Supports `${VAR_NAME}` syntax for runtime interpolation.
  ```json
  "env": {
    "API_KEY": "secret-key",
    "DATABASE_URL": "postgres://${DB_USER}@${DB_HOST}/mydb"
  }
  ```
</ParamField>

<ParamField body="cwd" type="string">
  Working directory for the server process. Relative paths are resolved from the config file location.
</ParamField>

<ParamField body="args" type="list[str]">
  Command-line arguments to pass to the server, passed after `--` to the server's argument parser.
  ```json
  "args": ["--config", "server-config.json"]
  ```
</ParamField>

Environment Variable Interpolation

The env field in deployment configuration supports runtime interpolation of environment variables using ${VAR_NAME} syntax. This enables dynamic configuration based on your deployment environment:

{
  "deployment": {
    "env": {
      "API_URL": "https://api.${ENVIRONMENT}.example.com",
      "DATABASE_URL": "postgres://${DB_USER}:${DB_PASS}@${DB_HOST}/myapp",
      "CACHE_KEY": "myapp_${ENVIRONMENT}_${VERSION}"
    }
  }
}

When the server starts, FastMCP replaces ${ENVIRONMENT}, ${DB_USER}, etc. with values from your system's environment variables. If a variable doesn't exist, the placeholder is preserved as-is.

Example: If your system has ENVIRONMENT=production and DB_HOST=db.example.com:

// Configuration
{
  "deployment": {
    "env": {
      "API_URL": "https://api.${ENVIRONMENT}.example.com",
      "DB_HOST": "${DB_HOST}"
    }
  }
}

// Result at runtime
{
  "API_URL": "https://api.production.example.com",
  "DB_HOST": "db.example.com"
}

This feature is particularly useful for:

• Deploying the same configuration across development, staging, and production
• Keeping sensitive values out of configuration files
• Building dynamic URLs and connection strings
• Creating environment-specific prefixes or suffixes

Usage with CLI Commands

FastMCP automatically detects and uses a file specifically named fastmcp.json in the current directory, making server execution simple and consistent. Files with FastMCP configuration format but different names are not auto-detected and must be specified explicitly:

# Auto-detect fastmcp.json in current directory
cd my-project
fastmcp run  # No arguments needed!

# Or specify a configuration file explicitly
fastmcp run prod.fastmcp.json

# Skip environment setup when already in a uv environment
fastmcp run fastmcp.json --skip-env

# Skip source preparation when source is already prepared
fastmcp run fastmcp.json --skip-source

# Skip both environment and source preparation
fastmcp run fastmcp.json --skip-env --skip-source

Pre-building Environments

You can use fastmcp project prepare to create a persistent uv project with all dependencies pre-installed:

# Create a persistent environment
fastmcp project prepare fastmcp.json --output-dir ./env

# Use the pre-built environment to run the server
fastmcp run fastmcp.json --project ./env

This pattern separates environment setup (slow) from server execution (fast), useful for deployment scenarios.

Using an Existing Environment

By default, FastMCP creates an isolated environment with uv based on your configuration. When you already have a suitable Python environment, use the --skip-env flag to skip environment creation:

fastmcp run fastmcp.json --skip-env

When you already have an environment:

• You're in an activated virtual environment with all dependencies installed
• You're inside a Docker container with pre-installed dependencies
• You're in a CI/CD pipeline that pre-builds the environment
• You're using a system-wide installation with all required packages
• You're in a uv-managed environment (prevents infinite recursion)

This flag tells FastMCP: "I already have everything installed, just run the server."

Using an Existing Source

When working with source types that require preparation (future support for git repositories or cloud sources), use the --skip-source flag when you already have the source code available:

fastmcp run fastmcp.json --skip-source

When you already have the source:

• You've previously cloned a git repository and don't need to re-fetch
• You have a cached copy of a cloud-hosted server
• You're in a CI/CD pipeline where source checkout is a separate step
• You're iterating locally on already-downloaded code

This flag tells FastMCP: "I already have the source code, skip any download/clone steps."

Note: For filesystem sources (local Python files), this flag has no effect since they don't require preparation.

The configuration file works with all FastMCP commands:

• run - Start the server in production mode
• dev - Launch with the Inspector UI for development
• inspect - View server capabilities and configuration
• install - Install to Claude Desktop, Cursor, or other MCP clients

When no file argument is provided, FastMCP searches the current directory for fastmcp.json. This means you can simply navigate to your project directory and run fastmcp run to start your server with all its configured settings.

CLI Override Behavior

Command-line arguments take precedence over configuration file values, allowing ad-hoc adjustments without modifying the file:

# Config specifies port 3000, CLI overrides to 8080
fastmcp run fastmcp.json --port 8080

# Config specifies stdio, CLI overrides to HTTP
fastmcp run fastmcp.json --transport http

# Add extra dependencies not in config
fastmcp run fastmcp.json --with requests --with httpx

This precedence order enables:

• Quick testing of different settings
• Environment-specific overrides in deployment scripts
• Debugging with increased log levels
• Temporary configuration changes

Custom Naming Patterns

You can use different configuration files for different environments:

• fastmcp.json - Default configuration
• dev.fastmcp.json - Development settings
• prod.fastmcp.json - Production settings
• test_fastmcp.json - Test configuration

Any file with "fastmcp.json" in the name is recognized as a configuration file.

Examples

A minimal configuration for a simple server:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "path": "server.py",
    "entrypoint": "mcp"
  }
}

This configuration explicitly specifies the server entrypoint (mcp), making it clear which server instance or factory function to use. Uses all defaults: STDIO transport, no special dependencies, standard logging. </Tab> <Tab title="Development Configuration">

A configuration optimized for local development:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  // WHERE does the server live?
  "source": {
    "path": "src/server.py",
    "entrypoint": "app"
  },
  // WHAT dependencies does it need?
  "environment": {
    "type": "uv",
    "python": "3.12",
    "dependencies": ["fastmcp[dev]"],
    "editable": "."
  },
  // HOW should it run?
  "deployment": {
    "transport": "http",
    "host": "127.0.0.1",
    "port": 8000,
    "log_level": "DEBUG",
    "env": {
      "DEBUG": "true",
      "ENV": "development"
    }
  }
}

A production-ready configuration with full dependency management:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  // WHERE does the server live?
  "source": {
    "path": "app/main.py",
    "entrypoint": "mcp_server"
  },
  // WHAT dependencies does it need?
  "environment": {
    "python": "3.11",
    "requirements": "requirements/production.txt",
    "project": "."
  },
  // HOW should it run?
  "deployment": {
    "transport": "http",
    "host": "0.0.0.0",
    "port": 3000,
    "path": "/api/mcp/",
    "log_level": "INFO",
    "env": {
      "ENV": "production",
      "API_BASE_URL": "https://api.example.com",
      "DATABASE_URL": "postgresql://user:[email protected]/prod"
    },
    "cwd": "/app",
    "args": ["--workers", "4"]
  }
}

Configuration for a data analysis server with scientific packages:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "path": "analysis_server.py",
    "entrypoint": "mcp"
  },
  "environment": {
    "python": "3.11",
    "dependencies": [
      "pandas>=2.0",
      "numpy",
      "scikit-learn",
      "matplotlib",
      "jupyterlab"
    ]
  },
  "deployment": {
    "transport": "stdio",
    "env": {
      "MATPLOTLIB_BACKEND": "Agg",
      "DATA_PATH": "./datasets"
    }
  }
}

You can maintain multiple configuration files for different environments:

dev.fastmcp.json:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "path": "server.py",
    "entrypoint": "mcp"
  },
  "deployment": {
    "transport": "http",
    "log_level": "DEBUG"
  }
}

prod.fastmcp.json:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "path": "server.py",
    "entrypoint": "mcp"
  },
  "environment": {
    "requirements": "requirements/production.txt"
  },
  "deployment": {
    "transport": "http",
    "host": "0.0.0.0",
    "log_level": "WARNING"
  }
}

Run different configurations:

fastmcp run dev.fastmcp.json   # Development
fastmcp run prod.fastmcp.json  # Production

Migrating from CLI Arguments

If you're currently using command-line arguments or shell scripts, migrating to fastmcp.json simplifies your workflow. Here's how common CLI patterns map to configuration:

CLI Command:

uv run --with pandas --with requests \
  fastmcp run server.py \
  --transport http \
  --port 8000 \
  --log-level INFO

Equivalent fastmcp.json:

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "path": "server.py",
    "entrypoint": "mcp"
  },
  "environment": {
    "dependencies": ["pandas", "requests"]
  },
  "deployment": {
    "transport": "http",
    "port": 8000,
    "log_level": "INFO"
  }
}

Now simply run:

fastmcp run  # Automatically finds and uses fastmcp.json

The configuration file approach provides better documentation, easier sharing, and consistent execution across different environments while maintaining the flexibility to override settings when needed.