# Model Context Protocol (MCP) Server Documentation
## Table of Contents
1. [What is MCP?](#what-is-mcp)
2. [Core Concepts](#core-concepts)
3. [MCP Architecture](#mcp-architecture)
4. [How MCP Servers Work](#how-mcp-servers-work)
5. [MCP vs Traditional APIs](#mcp-vs-traditional-apis)
6. [Building MCP Servers](#building-mcp-servers)
7. [Real-World Examples](#real-world-examples)
8. [Benefits and Use Cases](#benefits-and-use-cases)
9. [Implementation Guide](#implementation-guide)
10. [Best Practices](#best-practices)
---
## What is MCP?
**Model Context Protocol (MCP)** is a standardized communication protocol designed to enable AI models and applications to interact with external data sources, tools, and services in a consistent, secure, and extensible way.
### Key Definition
An **MCP Server** is a service that implements the MCP specification to provide AI models with access to specific capabilities, data, or tools through a standardized interface.
### Why MCP Exists
- **Standardization**: Provides a common way for AI models to access external resources
- **Security**: Controlled access to sensitive data and operations
- **Extensibility**: Easy to add new capabilities without changing core AI systems
- **Interoperability**: Works across different AI platforms and models
---
## Core Concepts
### 1. Protocol-Based Communication
MCP uses structured message passing between clients (AI models/applications) and servers (capability providers).
```json
{
"method": "operation_name",
"params": {
"parameter1": "value1",
"parameter2": "value2"
}
}
```
### 2. Capability Exposure
MCP servers expose specific capabilities through well-defined endpoints:
- **Tools**: Actions the AI can perform
- **Resources**: Data the AI can access
- **Prompts**: Pre-defined interaction patterns
### 3. Context Management
MCP maintains context about:
- Available capabilities
- Current session state
- Security permissions
- Resource metadata
---
## MCP Architecture
```
┌─────────────────┐ MCP Protocol ┌─────────────────┐
│ AI Model/ │ ◄─────────────────► │ MCP Server │
│ Application │ │ │
│ (Client) │ │ ┌───────────┐ │
└─────────────────┘ │ │ Capability│ │
│ │ Provider │ │
│ └───────────┘ │
│ ┌───────────┐ │
│ │ Data │ │
│ │ Source │ │
│ └───────────┘ │
│ ┌───────────┐ │
│ │ Tool │ │
│ │ Executor │ │
│ └───────────┘ │
└─────────────────┘
```
### Components
1. **MCP Client**: AI model or application that consumes capabilities
2. **MCP Server**: Service that provides capabilities
3. **Transport Layer**: Communication mechanism (HTTP, WebSocket, etc.)
4. **Capability Providers**: Actual implementations of tools/resources
---
## How MCP Servers Work
### 1. Server Initialization
```python
class MCPServer:
def __init__(self):
self.capabilities = {}
self.resources = {}
self.tools = {}
def register_capability(self, name, handler):
self.capabilities[name] = handler
```
### 2. Capability Registration
MCP servers register their available capabilities:
```python
# Register a tool
server.register_tool("search_documents", search_handler)
# Register a resource
server.register_resource("user_data", data_handler)
# Register a prompt template
server.register_prompt("summarize", summary_template)
```
### 3. Request Processing
When a client makes a request:
```python
async def handle_request(self, request):
method = request.get("method")
params = request.get("params", {})
if method in self.capabilities:
handler = self.capabilities[method]
result = await handler(params)
return {"status": "success", "result": result}
else:
return {"status": "error", "message": "Unknown method"}
```
### 4. Response Formatting
MCP servers return standardized responses:
```json
{
"status": "success",
"result": {
"data": "...",
"metadata": {...}
}
}
```
---
## MCP vs Traditional APIs
| Aspect | Traditional API | MCP Server |
|--------|----------------|------------|
| **Purpose** | General web services | AI model integration |
| **Protocol** | REST, GraphQL, etc. | MCP specification |
| **Context** | Stateless requests | Context-aware sessions |
| **Discovery** | Manual documentation | Automatic capability discovery |
| **Security** | API keys, OAuth | MCP-specific auth + permissions |
| **AI Integration** | Manual adaptation | Native AI model support |
### Example Comparison
**Traditional REST API:**
```http
GET /api/v1/documents/search?q=machine+learning
Authorization: Bearer token123
```
**MCP Server:**
```json
{
"method": "search_documents",
"params": {
"query": "machine learning",
"context": "research_project"
}
}
```
---
## Building MCP Servers
### 1. Define Your Capabilities
Identify what your server will provide:
- **Data Access**: Databases, files, APIs
- **Tool Execution**: Calculations, transformations, external services
- **Specialized Knowledge**: Domain-specific operations
### 2. Implement Core Structure
```python
class DocumentQAServer:
def __init__(self):
self.documents = {}
self.embeddings = {}
async def load_document(self, params):
file_path = params.get("file_path")
# Implementation here
return {"status": "success", "chunks": len(chunks)}
async def ask_question(self, params):
question = params.get("question")
# Implementation here
return {"answer": answer, "confidence": score}
```
### 3. Handle MCP Protocol
```python
async def handle_mcp_request(server, request):
method = request.get("method")
params = request.get("params", {})
if method == "load_document":
return await server.load_document(params)
elif method == "ask_question":
return await server.ask_question(params)
else:
return {"error": f"Unknown method: {method}"}
```
---
## Real-World Examples
### 1. Document Q&A Server (Our Implementation)
**Purpose**: Answer questions based on uploaded documents
**Capabilities**:
- `load_document`: Process and index documents
- `ask_question`: Answer questions using document context
- `get_status`: Report server state
### 2. Database MCP Server
**Purpose**: Provide AI access to database operations
**Capabilities**:
- `query_data`: Execute SQL queries
- `get_schema`: Return database structure
- `insert_record`: Add new data
### 3. Web Search MCP Server
**Purpose**: Enable AI to search the internet
**Capabilities**:
- `web_search`: Search web content
- `fetch_page`: Retrieve specific web pages
- `summarize_results`: Process search results
### 4. File System MCP Server
**Purpose**: Manage files and directories
**Capabilities**:
- `list_files`: Browse directories
- `read_file`: Access file contents
- `write_file`: Create/modify files
---
## Benefits and Use Cases
### Benefits
1. **Standardization**
- Consistent interface across different capabilities
- Easier integration with AI platforms
- Reduced development complexity
2. **Security**
- Controlled access to sensitive resources
- Permission-based capability exposure
- Audit trails for AI actions
3. **Modularity**
- Plug-and-play architecture
- Independent capability development
- Easy testing and maintenance
4. **Scalability**
- Distributed capability providers
- Load balancing across servers
- Horizontal scaling options
### Use Cases
1. **Enterprise AI Integration**
- Connect AI to internal databases
- Automate business processes
- Provide AI access to company knowledge
2. **Research and Development**
- Scientific data analysis
- Literature review automation
- Experimental data processing
3. **Content Management**
- Document processing and analysis
- Content generation and editing
- Knowledge base maintenance
4. **Customer Support**
- Automated ticket resolution
- Knowledge base queries
- Customer data access
---
## Implementation Guide
### Step 1: Design Your Server
```python
# Define what capabilities you'll provide
capabilities = {
"primary_function": "What is the main purpose?",
"data_sources": "What data will you access?",
"tools": "What operations will you perform?",
"security": "What permissions are needed?"
}
```
### Step 2: Implement Core Logic
```python
class MyMCPServer:
def __init__(self, config):
self.config = config
self.initialize_resources()
def initialize_resources(self):
# Set up databases, APIs, etc.
pass
async def handle_capability_1(self, params):
# Implement your first capability
pass
async def handle_capability_2(self, params):
# Implement your second capability
pass
```
### Step 3: Add MCP Protocol Support
```python
async def handle_mcp_request(server, request):
method = request.get("method")
params = request.get("params", {})
# Route to appropriate handler
if hasattr(server, f"handle_{method}"):
handler = getattr(server, f"handle_{method}")
return await handler(params)
else:
return {"error": f"Unknown method: {method}"}
```
### Step 4: Add Error Handling
```python
async def safe_handle_request(server, request):
try:
return await handle_mcp_request(server, request)
except Exception as e:
return {
"status": "error",
"message": str(e),
"type": type(e).__name__
}
```
### Step 5: Implement Transport Layer
```python
# For HTTP/Web interface
from starlette.applications import Starlette
from starlette.responses import JSONResponse
async def mcp_endpoint(request):
data = await request.json()
result = await safe_handle_request(server, data)
return JSONResponse(result)
```
---
## Best Practices
### 1. Security
- **Validate all inputs** to prevent injection attacks
- **Implement proper authentication** and authorization
- **Log all operations** for audit trails
- **Use secure communication** (HTTPS, WSS)
### 2. Error Handling
- **Provide clear error messages** with context
- **Use consistent error formats** across all endpoints
- **Handle timeouts and resource limits** gracefully
- **Implement retry mechanisms** for transient failures
### 3. Performance
- **Cache frequently accessed data** to reduce latency
- **Implement connection pooling** for external resources
- **Use async/await patterns** for I/O operations
- **Monitor resource usage** and implement limits
### 4. Documentation
- **Document all capabilities** with examples
- **Provide clear parameter specifications**
- **Include error codes and meanings**
- **Maintain version compatibility** information
### 5. Testing
- **Unit test each capability** independently
- **Integration test the full MCP flow**
- **Load test with realistic scenarios**
- **Test error conditions** and edge cases
### Example Test Structure
```python
import pytest
class TestMCPServer:
@pytest.fixture
def server(self):
return MyMCPServer(test_config)
async def test_capability_1(self, server):
request = {
"method": "capability_1",
"params": {"test": "data"}
}
result = await handle_mcp_request(server, request)
assert result["status"] == "success"
async def test_error_handling(self, server):
request = {"method": "invalid_method"}
result = await handle_mcp_request(server, request)
assert "error" in result
```
---
## Conclusion
MCP servers provide a powerful, standardized way to extend AI capabilities by connecting them to external resources and tools. They offer:
- **Consistent interfaces** for AI integration
- **Secure access** to sensitive resources
- **Modular architecture** for easy development
- **Scalable solutions** for enterprise needs
By following MCP specifications and best practices, you can build robust servers that seamlessly integrate with AI systems while maintaining security, performance, and reliability.
The Document Q&A server we built demonstrates these principles in action, showing how MCP can enable sophisticated AI-powered document analysis with a clean, standardized interface.
