Enterprise MCP Server
by jimestesblog
README.md
๏ปฟ# MCP Server - Model Context Protocol Server
**Last Updated: September 29, 2025**
Example of a highly configurable, deployment-ready Model Context Protocol (MCP) server built with Python and FastAPI. This server provides a **fully modular architecture** where tools and resources can be developed as **external packages**, installed independently, and configured dynamically without code changes. The server enables seamless communication with MCP-compatible clients through its extensible plugin system.
## Related Repositories
https://github.com/jimestesblog/mcp_weather_tool
https://github.com/jimestesblog/mcp_example_resources
## Features
- **๐๏ธ Modular Architecture**: Clean separation of concerns with dedicated modules for core functionality, server components, tools, and utilities
- **โ๏ธ Dynamic Tool Loading**: Configure tools via YAML/JSON without code changes
- **๐ Secure Configuration**: Environment variable-based API key management
- **๐ Deployment Ready**: Health endpoints, Docker support, and Kubernetes deployment
- **๐งฐ Built-in Tools**: Weather forecasting, financial data (Plaid), and extensible tool framework
- **๐ Dynamic Resources**: Support for public HTTP resources and internal MCP server resources with parameterization
- **๐ก FastAPI Integration**: RESTful endpoints with automatic OpenAPI documentation
- **๐ณ Container Support**: Docker and Kubernetes deployment configurations
## Getting Started
### Quick Start
Get your MCP Server running in minutes:
#### 1. Installation
**Option A: Clone and Install from Source**
```bash
# Clone the repository
git clone <repository-url>
cd MCPServer
# Create virtual environment
python -m venv .venv
# Activate virtual environment
# Windows
.\.venv\Scripts\activate
# Linux/macOS
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
**Option B: Install as Package** (Coming Soon. when available on PyPI)
```bash
pip install mcp-server
```
#### 2. Configuration
Create or modify your configuration file:
```bash
# Copy example configuration
cp config/tools.yaml.example config/tools.yaml
# Edit configuration with your API keys and settings
notepad config/tools.yaml # Windows
nano config/tools.yaml # Linux/macOS
```
Example basic configuration:
```yaml
Domains:
- Name: WEATHER
Description: Weather information tools
mcp_classes:
- Domain: WEATHER
class_type: mcp_server.tools.weather.google_weather.GoogleWeatherTool
class_name: google_weather
class_description: Google Weather API integration
class_initialization_params:
params:
api_key: "${GOOGLE_WEATHER_API_KEY}"
tools:
- function: current_conditions
function_description: Get current weather conditions
tool_parameters:
- name: latitude
description: Latitude coordinate
allowed_values: string
- name: longitude
description: Longitude coordinate
allowed_values: string
```
#### 3. Environment Variables
Set up required environment variables:
```bash
# Windows
set GOOGLE_WEATHER_API_KEY=your-api-key-here
set LOG_LEVEL=info
# Linux/macOS
export GOOGLE_WEATHER_API_KEY=your-api-key-here
export LOG_LEVEL=info
```
#### 4. Run the Server
Start the MCP Server:
```bash
# Run directly
python -m mcp_server
# Or with custom configuration
python -m mcp_server --config config/tools.yaml
# Or with specific port
python -m mcp_server --port 8080
```
#### 5. Verify Installation
Test that your server is running:
```bash
# Check health endpoint
curl http://localhost:3000/health
# List available tools
curl http://localhost:3000/tools
# Test MCP endpoint
curl -X POST http://localhost:3000/mcp \
-H "Content-Type: application/json" \
-d '{"method": "tools/list"}'
```
### Docker Installation
Run with Docker for containerized deployment:
```bash
# Build the image
docker build -t mcp-server .
# Run with environment variables
docker run -d \
--name mcp-server \
-p 3000:3000 \
-e GOOGLE_WEATHER_API_KEY=your-api-key \
-e LOG_LEVEL=info \
mcp-server
# Check logs
docker logs mcp-server
```
### Kubernetes Deployment
Deploy to Kubernetes cluster:
```bash
# Create namespace
kubectl create namespace mcp-server
# Apply configuration with your API keys
kubectl apply -f k8s/deployment.yaml -n mcp-server
# Check deployment status
kubectl get pods -n mcp-server
# Access via port forward
kubectl port-forward service/mcp-server 3000:3000 -n mcp-server
```
### Adding Your First Custom Tool
1. **Create a new tool file**:
```python
# my_custom_tool.py
from mcp_server.tools.enhanced_base import Tool, ToolConfig
from typing import Dict, Any
class HelloWorldTool(Tool):
async def execute(self, **kwargs) -> Dict[str, Any]:
name = kwargs.get("name", "World")
return {"message": f"Hello, {name}!"}
def get_schema(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"name": {"type": "string", "description": "Name to greet"}
}
}
```
2. **Add to configuration**:
```yaml
mcp_classes:
- Domain: DEMO
class_type: my_custom_tool.HelloWorldTool
class_name: hello_world
class_description: Simple hello world tool
tools:
- function: execute
function_description: Say hello
tool_parameters:
- name: name
description: Name to greet
allowed_values: string
```
3. **Restart server and test**:
```bash
# Test your new tool
curl -X POST http://localhost:3000/mcp \
-H "Content-Type: application/json" \
-d '{
"method": "tools/call",
"params": {
"name": "hello_world.execute",
"arguments": {"name": "MCP User"}
}
}'
```
### Troubleshooting Common Issues
**Server won't start**:
- Check that all environment variables are set
- Verify configuration file syntax (YAML format)
- Ensure no port conflicts (default port 3000)
**Tool loading fails**:
- Verify `class_type` paths are correct
- Check that tool classes inherit from `Tool` base class
- Ensure all dependencies are installed
**API calls fail**:
- Verify API keys are valid and properly configured
- Check network connectivity and firewall settings
- Review rate limits and API quotas
**Need help?**:
- Check server logs: `docker logs mcp-server` or console output
- Review configuration against examples in this README
- Validate JSON/YAML syntax in configuration files
## Architecture
The MCP Server follows a clean, modular architecture designed for maintainability, security, and extensibility:
```
mcp_server/
โโโ core/ # Core functionality
โ โโโ config.py # Centralized configuration management with Pydantic
โ โโโ exceptions.py # Custom exception hierarchy
โ โโโ schemas.py # JSON schema builders for tool parameters
โโโ server/ # Server components
โ โโโ app.py # FastAPI application and server lifecycle
โ โโโ factory.py # ServerFactory for domain and tool registration
โโโ tools/ # Tool implementations
โ โโโ enhanced_base.py # Abstract base classes with proper typing
โ โโโ registry.py # Simplified tool registration system
โ โโโ base.py # Legacy base classes (backward compatibility)
โ โโโ plaid.py # Financial data tool (Plaid API)
โ โโโ weather/ # Weather tools
โ โโโ google_weather.py # Google Weather API integration
โโโ utils/ # Utility functions
โโโ strings.py # String manipulation utilities
โโโ imports.py # Dynamic import helpers
```
### Key Architectural Components
#### Configuration Management (`core/config.py`)
- **Pydantic-based validation**: Type-safe configuration with automatic validation
- **Environment variable expansion**: Secure API key management using `${VARIABLE_NAME}` syntax
- **Legacy compatibility**: Supports both new and old configuration formats
- **Centralized loading**: Single point of configuration management
#### Server Factory (`server/factory.py`)
- **Domain management**: Automatic creation and mounting of MCP domains
- **Tool registration**: Dynamic tool instantiation and registration
- **Error handling**: Comprehensive error reporting with context
- **Session management**: Proper lifecycle management for MCP sessions
#### Tool Registry (`tools/registry.py`)
- **Dynamic registration**: Runtime tool class registration from configuration
- **Type safety**: Ensures all tools inherit from proper base classes
- **Instance management**: Centralized tool instance lifecycle
- **Import utilities**: Safe dynamic imports with error handling
## Developing External Packages
The MCP Server's modular architecture enables you to develop tools and resources as **external packages** that can be installed independently and configured dynamically. This approach promotes code reusability, maintainability, and allows for distributed development of MCP components.
### Why External Packages?
- **๐ Plug-and-Play**: Install only the tools you need
- **๐ฆ Distribution**: Share tools via PyPI or private repositories
- **๐ Versioning**: Independent versioning for each tool package
- **๐ฅ Collaboration**: Teams can develop tools independently
- **๐งช Testing**: Isolated testing environments per package
- **๐ Security**: Minimal dependencies and controlled access
### Package Structure
A typical external MCP tool package follows this structure:
```
my-mcp-tools/
โโโ setup.py # Package configuration
โโโ README.md # Package documentation
โโโ requirements.txt # Dependencies
โโโ my_mcp_tools/ # Package root
โ โโโ __init__.py
โ โโโ tools/ # Tool implementations
โ โ โโโ __init__.py
โ โ โโโ custom_api.py # Custom API tool
โ โ โโโ data_processor.py # Data processing tool
โ โโโ resources/ # Resource implementations
โ โโโ __init__.py
โ โโโ external_data.py # External data resource
โโโ tests/ # Package tests
โ โโโ test_tools.py
โ โโโ test_resources.py
โโโ config/ # Example configurations
โโโ tools.yaml.example
```
### Creating an External Tool Package
#### 1. Package Setup (`setup.py`)
```python
from setuptools import setup, find_packages
setup(
name="my-custom-mcp-tools",
version="1.0.0",
description="Custom MCP tools for XYZ integration",
author="Your Name",
packages=find_packages(),
python_requires=">=3.8",
install_requires=[
"mcp-server>=2.0.0", # Base MCP server dependency
"httpx>=0.24.0", # For HTTP requests
"pydantic>=2.0.0", # For data validation
# Add your specific dependencies
],
classifiers=[
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
],
)
```
#### 2. Tool Implementation
```python
# my_mcp_tools/tools/custom_api.py
from mcp_server.tools.enhanced_base import Tool, ToolConfig
from typing import Dict, Any
import httpx
class CustomAPITool(Tool):
"""Custom API integration tool."""
def __init__(self, config: ToolConfig):
super().__init__(config)
self.api_key = config.params.get("api_key")
self.base_url = config.params.get("base_url")
async def execute(self, **kwargs) -> Dict[str, Any]:
"""Execute API call with given parameters."""
endpoint = kwargs.get("endpoint", "")
method = kwargs.get("method", "GET").upper()
url = f"{self.base_url}/{endpoint.lstrip('/')}"
headers = {"Authorization": f"Bearer {self.api_key}"}
async with httpx.AsyncClient() as client:
if method == "GET":
response = await client.get(url, headers=headers)
elif method == "POST":
data = kwargs.get("data", {})
response = await client.post(url, json=data, headers=headers)
return {
"status_code": response.status_code,
"data": response.json() if response.status_code == 200 else None,
"error": None if response.status_code == 200 else response.text
}
def get_schema(self) -> Dict[str, Any]:
"""Return JSON schema for tool parameters."""
return {
"type": "object",
"properties": {
"endpoint": {
"type": "string",
"description": "API endpoint path"
},
"method": {
"type": "string",
"enum": ["GET", "POST", "PUT", "DELETE"],
"default": "GET"
},
"data": {
"type": "object",
"description": "Request payload for POST/PUT"
}
},
"required": ["endpoint"]
}
```
#### 3. Package Installation and Configuration
Once your package is ready, install it in your MCP server environment:
```bash
# Install from local development
pip install -e ./my-mcp-tools
# Or install from PyPI
pip install my-custom-mcp-tools
# Or install from Git repository
pip install git+https://github.com/yourorg/my-mcp-tools.git
```
#### 4. Configure in MCP Server
Add your external tool to `config/tools.yaml`:
```yaml
Domains:
- Name: CUSTOM_API
Description: Custom API integration tools
mcp_classes:
- Domain: CUSTOM_API
class_type: my_mcp_tools.tools.custom_api.CustomAPITool
class_name: custom_api_tool
class_description: Custom API integration tool
class_initialization_params:
params:
api_key: "${CUSTOM_API_KEY}"
base_url: "https://api.example.com"
tools:
- function: execute
function_description: Execute custom API calls
tool_parameters:
- name: endpoint
description: API endpoint to call
allowed_values: string
- name: method
description: HTTP method
allowed_values: ["GET", "POST", "PUT", "DELETE"]
- name: data
description: Request payload
allowed_values: object
```
### Dynamic Loading Process
The MCP Server uses dynamic imports to load external packages:
1. **Configuration Parsing**: Server reads `class_type` from configuration
2. **Dynamic Import**: Uses `importlib` to load the specified module and class
3. **Validation**: Ensures the class inherits from proper base classes
4. **Registration**: Registers the tool in the internal registry
5. **Instantiation**: Creates tool instances with provided configuration
6. **Integration**: Makes tools available through MCP protocol
This process allows for complete decoupling between the server core and tool implementations.
### Best Practices for External Packages
- **Use proper base classes**: Always inherit from `Tool` or `Resource`
- **Handle errors gracefully**: Implement comprehensive error handling
- **Document thoroughly**: Include docstrings and README documentation
- **Version dependencies**: Pin dependency versions for stability
- **Test extensively**: Include unit and integration tests
- **Follow naming conventions**: Use descriptive names for classes and methods
- **Secure configuration**: Use environment variables for sensitive data
## Creating New Tools
### Quick Start
1. **Create your tool class** inheriting from the enhanced base:
```python
# my_custom_tool.py
from mcp_server.tools.enhanced_base import Tool, ToolConfig
from typing import Dict, Any
class MyCustomTool(Tool):
"""Custom tool for demonstration."""
def __init__(self, config: ToolConfig):
super().__init__(config)
# Access configuration parameters
self.api_key = config.params.get("api_key")
self.base_url = config.params.get("base_url", "https://api.example.com")
async def execute(self, **kwargs) -> Dict[str, Any]:
"""Execute the tool with given parameters."""
action = kwargs.get("action", "default")
if action == "get_data":
return await self._get_data(kwargs.get("query", ""))
elif action == "process":
return await self._process_data(kwargs.get("data", {}))
else:
return {"error": f"Unknown action: {action}"}
def get_schema(self) -> Dict[str, Any]:
"""Return JSON schema for tool parameters."""
return {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": ["get_data", "process"],
"description": "Action to perform"
},
"query": {
"type": "string",
"description": "Query parameter for get_data action"
},
"data": {
"type": "object",
"description": "Data to process"
}
},
"required": ["action"],
"additionalProperties": True
}
async def _get_data(self, query: str) -> Dict[str, Any]:
"""Private method to fetch data."""
# Implement your API call logic here
return {"result": f"Data for query: {query}"}
async def _process_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Private method to process data."""
# Implement your processing logic here
return {"processed": data, "status": "complete"}
```
2. **Add tool configuration** to your `config/tools.yaml`:
```yaml
Domains:
- Name: CUSTOM
Description: Custom tools domain
mcp_classes:
- Domain: CUSTOM
class_type: path.to.your.module.MyCustomTool
class_name: my_custom_tool
class_description: Custom tool for demonstration purposes
class_initialization_params:
params:
api_key: "${MY_TOOL_API_KEY}"
base_url: "https://api.example.com"
timeout: 30
tools:
- function: execute
function_description: Execute custom tool operations
tool_parameters:
- name: action
description: Action to perform
allowed_values: ["get_data", "process"]
- name: query
description: Query parameter for get_data
allowed_values: string
- name: data
description: Data object for process
allowed_values: object
```
3. **Set environment variables**:
```bash
# Windows
set MY_TOOL_API_KEY=your-api-key-here
# Linux/Mac
export MY_TOOL_API_KEY=your-api-key-here
```
### MCP Tool Output Schemas
Starting with version 2025-09-25, the MCP Server supports explicit output schema configuration for tools. This enhancement allows you to define structured response schemas that MCP clients can use to better understand and validate tool outputs.
#### Configuring Output Schemas
You can define explicit output schemas in your `tools.yaml` configuration using the `tool_output_schema` field:
```yaml
tools:
- function: _current_conditions
function_description: Get current weather conditions
tool_parameters:
- name: latitude
description: Latitude in decimal degrees
allowed_values: string
- name: longitude
description: Longitude in decimal degrees
allowed_values: string
tool_output_schema:
title: "Weather API Response Schema"
description: "Structured weather data response"
type: "object"
properties:
status_code:
type: "integer"
description: "HTTP status code"
headers:
type: "object"
description: "Response headers"
additionalProperties: true
body:
type: "object"
description: "Weather data payload"
properties:
temperature:
type: "object"
properties:
degrees:
type: "number"
description: "Temperature value"
unit:
type: "string"
enum: ["FAHRENHEIT", "CELSIUS"]
required: ["degrees", "unit"]
weatherCondition:
type: "object"
properties:
type:
type: "string"
enum: ["CLEAR", "CLOUDY", "RAIN", "SNOW"]
description:
type: "object"
properties:
text:
type: "string"
description: "Human-readable condition"
required: ["text"]
required: ["type", "description"]
required: ["temperature", "weatherCondition"]
required: ["status_code", "headers", "body"]
```
#### Key Features
- **JSON Schema Validation**: Output schemas follow JSON Schema Draft 07 specification
- **Override Detection**: The system logs warnings when explicit schemas override implicit class-based schemas
- **Optional Configuration**: The `tool_output_schema` field is optional and doesn't affect existing functionality
- **Structured Responses**: Helps MCP clients understand and validate tool response formats
#### Schema Definition Guidelines
1. **Use descriptive titles and descriptions** for better documentation
2. **Define required fields** to ensure consistent response structure
3. **Use enums for constrained values** to improve validation
4. **Include nested object definitions** for complex data structures
5. **Set appropriate data types** (string, number, integer, boolean, object, array)
#### Warning Messages
When an explicit output schema is defined for a tool that already has an implicit schema method (like `get_output_schema()` in the tool class), the system will log a warning:
```
WARNING: Tool google_weather.current_conditions: Explicit output schema defined in configuration overrides implicit schema from tool class method
```
This helps developers identify potential conflicts between configuration-based and code-based schema definitions.
### Advanced Tool Patterns
#### Specialized Base Classes
For tools with common functionality, create specialized base classes:
```python
from mcp_server.tools.enhanced_base import Tool, ToolConfig
class APITool(Tool):
"""Base class for API-based tools."""
def __init__(self, config: ToolConfig):
super().__init__(config)
self.api_key = config.params.get("api_key")
self.base_url = config.params.get("base_url", "")
self.timeout = config.params.get("timeout", 30)
async def make_request(self, endpoint: str, params: Dict[str, Any]) -> Dict[str, Any]:
"""Common HTTP request logic."""
import httpx
url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
headers = {"Authorization": f"Bearer {self.api_key}"}
async with httpx.AsyncClient(timeout=self.timeout) as client:
response = await client.get(url, params=params, headers=headers)
return {
"status_code": response.status_code,
"data": response.json() if response.status_code == 200 else None,
"error": None if response.status_code == 200 else response.text
}
```
#### Multiple Function Tools
For tools with multiple functions (like GoogleWeatherTool):
```yaml
tools:
- function: _current_data
function_description: Get current data
tool_parameters:
- name: location
description: Location parameter
allowed_values: string
- function: _historical_data
function_description: Get historical data
tool_parameters:
- name: location
description: Location parameter
allowed_values: string
- name: days
description: Number of days
allowed_values: number
```
#### Parameter Validation
Use Pydantic for advanced parameter validation:
```python
from pydantic import BaseModel, Field, validator
from typing import Optional
class ToolParams(BaseModel):
"""Validation model for tool parameters."""
location: str = Field(..., min_length=1, description="Location name")
days: Optional[int] = Field(default=7, ge=1, le=30, description="Number of days")
units: str = Field(default="metric", pattern="^(metric|imperial)$")
@validator('location')
def validate_location(cls, v):
if not v.strip():
raise ValueError('Location cannot be empty')
return v.strip()
class MyValidatedTool(Tool):
async def execute(self, **kwargs) -> Dict[str, Any]:
try:
# Validate parameters using Pydantic
params = ToolParams(**kwargs)
return await self._process_validated_params(params)
except Exception as e:
return {"error": f"Parameter validation failed: {str(e)}"}
```
## Resources
The MCP Server supports dynamic resource configuration, allowing access to both public HTTP resources and internal MCP server resources. Resources provide a way to access external data sources and internal content through a unified interface.
### Resource Types
#### Public Resources
- **Access Type**: `public`
- **Description**: Resources accessible directly from the internet with HTTP/HTTPS URLs
- **Use Cases**: Public APIs, data files, web content
- **Example**: CSV files from GitHub, public API endpoints
#### MCP Server Resources
- **Access Type**: `mcp_server`
- **Description**: Resources where content is generated by internal MCP server functions
- **Use Cases**: Dynamic content generation, parameterized responses, business logic
- **Example**: Client-specific configurations, processed data
### Creating Resources
#### 1. Public HTTP Resource
```python
# mcp_server/resources/example1/publichttpresource.py
from mcp_server.resources.base import Resource
import httpx
class HttpResource(Resource):
"""Resource class for accessing public HTTP/HTTPS resources."""
async def get_resource_content(self, resource_name: str, parameters=None) -> str:
"""Get content for a specific named resource."""
resource_config = self._find_resource_config(resource_name)
url = resource_config.uri
# Substitute parameters if provided
if parameters:
url = self.substitute_parameters(url, parameters)
async with httpx.AsyncClient() as client:
response = await client.get(url)
if response.status_code == 200:
return response.text
else:
raise ValueError(f"HTTP error {response.status_code}")
```
#### 2. MCP Server Resource
```python
# mcp_server/resources/example1/privateresourceexample.py
from mcp_server.resources.base import Resource
class ExamplePrivateResources(Resource):
"""Resource class for internal MCP server resources."""
async def _sample_parameterized_resource(self, parameters) -> str:
"""Generate content based on client parameter."""
client = parameters.get("client", "").lower()
if client == "acme":
return "This is the roadrunner client"
elif client == "bigrock":
return "We make tools to smash birds"
else:
return f"Unknown client: {client}"
```
### Configuration
Resources are configured in the `tools.yaml` file alongside tools. Each resource class can manage multiple resources.
#### Basic Resource Configuration
```yaml
Domains:
- Name: USECASEY
Description: Example resource domain
mcp_classes:
# Public HTTP Resource Class
- Domain: USECASEY
class_type: mcp_server.resources.example1.publichttpresource.HttpResource
class_name: http_resources
class_description: Public example resource types.
class_initialization_params:
params:
tools:
resources:
- name: weather_data
description: Static weather data from GitHub.
type: csv
access: public
uri: https://raw.githubusercontent.com/velicki/Weather_Data_Analysis_Project/refs/heads/main/Weather_Data.csv
# Private MCP Server Resource Class
- Domain: USECASEY
class_type: mcp_server.resources.example1.privateresourceexample.ExamplePrivateResources
class_name: example_private_resources
class_description: Example private resource types.
class_initialization_params:
params:
tools:
resources:
- name: sample_parameterized_resource
description: Sample parameterized resource with client-specific responses.
function: _sample_parameterized_resource
type: txt
access: mcp_server
uri: //sampledata/{client}/
resource_parameters:
- name: client
description: Client ID for personalized content.
allowed_values: string
```
### Resource Parameters
Resources support parameterization through URI templates and parameter definitions:
#### Parameter Substitution
- Use `{parameter_name}` in URIs for parameter substitution
- Parameters are defined in the `resource_parameters` section
- The system automatically substitutes parameter values in URIs
#### Parameter Configuration
```yaml
resource_parameters:
- name: client
description: Client identifier
allowed_values: string
- name: format
description: Output format
allowed_values: [json, xml, csv]
- name: limit
description: Maximum number of results
allowed_values: number
```
### Resource Architecture
```
mcp_server/
โโโ resources/ # Resource implementations
โ โโโ base.py # Abstract base classes and configuration
โ โโโ registry.py # Resource registration system
โ โโโ example1/ # Example resource implementations
โ โโโ publichttpresource.py # Public HTTP resources
โ โโโ privateresourceexample.py # Private MCP resources
```
#### Key Components
##### Resource Base Classes (`resources/base.py`)
- **ResourceConfig**: Pydantic model for resource configuration
- **ResourceParameter**: Parameter definition and validation
- **Resource**: Abstract base class for all resources
- **ResourceAccessType**: Enum for access types (public, mcp_server)
##### Resource Registry (`resources/registry.py`)
- **ResourceRegistry**: Manages resource class registration and instances
- **Dynamic loading**: Import resource classes from configuration
- **Error handling**: Comprehensive error reporting for resource issues
### Usage Examples
#### Fetching Public Resource Content
```python
from mcp_server.resources.example1.publichttpresource import HttpResource
# Configure resource
config = {
"name": "http_resources",
"params": {
"resources": [
{
"name": "weather_data",
"type": "csv",
"access": "public",
"uri": "https://example.com/weather.csv"
}
]
}
}
# Create and use resource
resource = HttpResource(config)
content = await resource.get_resource_content("weather_data")
```
#### Using Parameterized Resources
```python
from mcp_server.resources.example1.privateresourceexample import ExamplePrivateResources
# Configure parameterized resource
config = {
"name": "private_resources",
"params": {
"resources": [
{
"name": "client_config",
"function": "_sample_parameterized_resource",
"type": "txt",
"access": "mcp_server",
"uri": "//config/{client}/",
"resource_parameters": [
{"name": "client", "description": "Client ID", "allowed_values": "string"}
]
}
]
}
}
# Use with parameters
resource = ExamplePrivateResources(config)
content = await resource.get_resource_content("client_config", {"client": "acme"})
# Returns: "This is the roadrunner client"
```
### Testing Resources
Use the provided test script to verify resource functionality:
```bash
python test_resources.py
```
The test script validates:
- Public HTTP resource fetching
- Parameterized MCP server resources
- Configuration loading and parsing
- Parameter substitution and validation
### Advanced Resource Patterns
#### Multi-Resource Classes
A single resource class can manage multiple related resources:
```yaml
resources:
- name: current_data
description: Current data endpoint
type: json
access: public
uri: https://api.example.com/current
- name: historical_data
description: Historical data with date range
type: json
access: public
uri: https://api.example.com/history/{start_date}/{end_date}
resource_parameters:
- name: start_date
description: Start date (YYYY-MM-DD)
allowed_values: string
- name: end_date
description: End date (YYYY-MM-DD)
allowed_values: string
```
#### Environment-Based Configuration
```yaml
class_initialization_params:
params:
api_key: "${EXTERNAL_API_KEY}"
base_url: "${EXTERNAL_API_URL}"
```
#### Custom Resource Types
Extend the base Resource class for specialized functionality:
```python
from mcp_server.resources.base import Resource
import json
class JsonApiResource(Resource):
"""Specialized resource for JSON APIs."""
async def get_content(self, parameters=None):
content = await super().get_content(parameters)
# Parse and validate JSON
try:
data = json.loads(content)
return self._process_json_data(data)
except json.JSONDecodeError:
raise ValueError("Invalid JSON response")
def _process_json_data(self, data):
"""Override to add custom JSON processing."""
return json.dumps(data, indent=2)
```
## Installation & Setup
### Prerequisites
- Python 3.10 or higher
- pip or conda package manager
### Method 1: Virtual Environment (Recommended)
```bash
# Create virtual environment
python -m venv .venv
# Activate virtual environment
# Windows
.\.venv\Scripts\activate
# Linux/Mac
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
### Method 2: Conda Environment
```bash
# Create environment from environment.yml
conda env create -f environment.yml
# Activate environment
conda activate MCPServer
```
### Environment Variables
Set required environment variables for your tools:
```bash
# Required for Google Weather Tool
set GOOGLE_WEATHER_API_KEY=your-weather-api-key
set GOOGLE_MAPS_API_KEY=your-maps-api-key
# Required for Plaid Tool
set PLAID_CLIENT_ID=your-plaid-client-id
set PLAID_SECRET=your-plaid-secret
# Optional: Custom config path
set CONFIG_PATH=config/tools.secure.yaml
```
## Usage
### Running the Server
#### Development Mode
```bash
# Using new modular architecture (recommended)
python -m mcp_server.server.app
# Using legacy interface (deprecated, shows warnings)
python -m mcp_server
```
#### Production Mode
```bash
# Set production environment
set LOG_LEVEL=info
set HOST=0.0.0.0
set HEALTH_PORT=8080
# Run server
python -m mcp_server.server.app
```
### Configuration
#### Secure Configuration (Recommended)
Use `config/tools.secure.yaml` with environment variables:
```yaml
# config/tools.secure.yaml
Domains:
- Name: WEATHER
Description: Weather and geocoding tools
mcp_classes:
- Domain: WEATHER
class_type: mcp_server.tools.weather.google_weather.GoogleWeatherTool
class_name: google_weather
class_description: Google Weather API tools
class_initialization_params:
params:
api_key: "${GOOGLE_WEATHER_API_KEY}"
geocoding_api_key: "${GOOGLE_MAPS_API_KEY}"
base_url: https://weather.googleapis.com/v1
```
#### Legacy Configuration
The server maintains backward compatibility with the original format:
```yaml
# config/tools.yaml (legacy format)
Domains:
- Name: WEATHER
Description: Weather tools
mcp_classes:
- Domain: WEATHER
class_type: mcp_server.tools.weather.google_weather.GoogleWeatherTool
# ... configuration continues
```
### Health Endpoints
The server provides health check endpoints:
- **Liveness**: `GET /healthz` - Returns `{"ok": true, "status": "healthy"}`
- **Readiness**: `GET /readyz` - Returns `{"ok": true, "status": "ready"}`
- **MCP Mounts**: `GET /mcp` - Lists all mounted MCP endpoints
```bash
# Check server health
curl http://localhost:8080/healthz
curl http://localhost:8080/readyz
curl http://localhost:8080/mcp
```
## Docker Deployment
### Building the Image
```bash
docker build -t mcp-server:latest .
```
### Running with Docker
```bash
# Run with environment variables
docker run -d \
--name mcp-server \
-p 8080:8080 \
-e GOOGLE_WEATHER_API_KEY=your-key \
-e GOOGLE_MAPS_API_KEY=your-key \
-e CONFIG_PATH=/app/config/tools.secure.yaml \
mcp-server:latest
```
### Docker Compose
```yaml
# docker-compose.yml
version: '3.8'
services:
mcp-server:
build: ..
ports:
- "8080:8080"
environment:
- GOOGLE_WEATHER_API_KEY=${GOOGLE_WEATHER_API_KEY}
- GOOGLE_MAPS_API_KEY=${GOOGLE_MAPS_API_KEY}
- CONFIG_PATH=/app/config/tools.yaml
volumes:
- ./config:/app/config:ro
```
## Kubernetes Deployment
Deploy using the provided Kubernetes manifest:
```bash
# Apply deployment
kubectl apply -f k8s/deployment.yaml
# Check status
kubectl get pods -l app=mcp-server
kubectl get services mcp-server
# View logs
kubectl logs -f deployment/mcp-server
```
## API Documentation
### MCP Protocol Endpoints
Each domain is mounted under `/mcp/{domain-slug}`:
- **Weather Tools**: `/mcp/weather`
- **Financial Tools**: `/mcp/financial` (if Plaid is configured)
- **Custom Domains**: `/mcp/{your-domain-slug}`
### Tool Invocation
Tools are invoked through the MCP protocol with the following pattern:
```json
{
"method": "tools/call",
"params": {
"name": "google_weather.current_conditions",
"arguments": {
"latitude": 37.7749,
"longitude": -122.4194,
"unitsSystem": "imperial"
}
}
}
```
### Available Tools
#### Google Weather Tool
**Functions:**
- `current_conditions` - Get current weather conditions
- `hourly_forecast` - Get hourly forecast (up to 48 hours)
- `daily_forecast` - Get daily forecast (up to 10 days)
- `geocode` - Convert address to coordinates
**Example Usage:**
```json
{
"name": "google_weather.current_conditions",
"arguments": {
"location": "Seattle, WA"
}
}
```
#### Plaid Tool
**Functions:**
- `create_link_token` - Create link token for Plaid Link
- `exchange_public_token` - Exchange public token for access token
- `get_accounts` - Get account information
- `get_balances` - Get account balances
- `get_transactions` - Get transaction history
## Development
### Project Structure
```
โโโ mcp_server/ # Main package
โโโ config/ # Configuration files
โโโ tests/ # Test suite
โโโ scripts/ # Development scripts
โโโ k8s/ # Kubernetes manifests
โโโ requirements.txt # Python dependencies
โโโ pyproject.toml # Project configuration
โโโ Dockerfile # Container definition
```
### Testing
```bash
# Install test dependencies
pip install pytest pytest-asyncio coverage
# Run tests
python -m pytest tests/ -v
# Run with coverage
coverage run -m pytest tests/
coverage report -m
coverage html # Generate HTML report
```
### Code Quality
The project follows Python best practices:
- **Type hints**: Full typing with mypy support
- **Pydantic validation**: Runtime type checking and validation
- **Error handling**: Comprehensive exception hierarchy
- **Documentation**: Docstrings for all public methods
- **Security**: Environment-based secrets management
### Adding New Domains
1. **Define domain** in configuration:
```yaml
Domains:
- Name: MY_DOMAIN
Description: Custom domain for my tools
```
2. **Create tools** for the domain:
```python
# my_domain_tool.py
class MyDomainTool(Tool):
# Implementation here
pass
```
3. **Register in configuration**:
```yaml
mcp_classes:
- Domain: MY_DOMAIN
class_type: path.to.my_domain_tool.MyDomainTool
# ... configuration
```
## Migration from Legacy Architecture
If upgrading from the original monolithic version:
### Automatic Migration
The server provides backward compatibility:
- Old configuration formats are automatically converted
- Legacy imports show deprecation warnings
- All original functionality is preserved
### Manual Migration (Recommended)
1. **Update imports**:
```python
# Old
from mcp_server.app import run
# New
from mcp_server.server.app import run
```
2. **Use secure configuration**:
```yaml
# Move from config/tools.yaml to config/tools.secure.yaml
# Replace hardcoded API keys with environment variables
api_key: "${GOOGLE_WEATHER_API_KEY}"
```
3. **Update tool class paths**:
```yaml
# Old
class_type: mcp_server.tools.google_weather.GoogleWeatherTool
# New
class_type: mcp_server.tools.weather.google_weather.GoogleWeatherTool
```
## Troubleshooting
### Common Issues
**Tool Loading Failures**
- Verify `class_type` paths are correct
- Check that all required environment variables are set
- Ensure tool classes inherit from proper base classes
**Configuration Errors**
- Validate YAML syntax
- Check environment variable names and values
- Verify file paths and permissions
**API Connection Issues**
- Confirm API keys are valid and properly set
- Check network connectivity and firewall settings
- Review rate limits and quotas
**Health Check Failures**
- Verify server is running on expected port
- Check for port conflicts
- Review server logs for startup errors
### Logging
Enable detailed logging for debugging:
```bash
set LOG_LEVEL=debug
python -m mcp_server.server.app
```
### Getting Help
1. **Check the logs** for detailed error messages
2. **Verify configuration** against examples in this README
3. **Test with curl** to isolate issues
4. **Review the source code** - it's well-documented!
## Contributing
1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request
### Development Setup
```bash
# Clone repository
git clone <repository-url>
cd MCPServer
# Setup development environment
python -m venv .venv
.\.venv\Scripts\activate # Windows
pip install -r requirements.txt
# Install pre-commit hooks (if available)
pre-commit install
# Run tests
python -m pytest tests/
```
## License
[Add your license information here]
## Changelog
### v2.0.0 (September 24, 2025)
- **Major Architecture Refactoring**: Complete modular redesign
- **Security Enhancements**: Environment-based configuration
- **Enhanced Tool Framework**: Improved base classes and type safety
- **Backward Compatibility**: Legacy support with deprecation warnings
- **Documentation**: Comprehensive README and code documentation
### v1.0.0 (Previous)
- Initial monolithic implementation
- Basic tool support
- Configuration via YAML files
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Maintenance
ActivityInactive
ResponsivenessNo issues