implementation_guide.md•10.6 kB
# LocalMCP Implementation Guide
## What We Need to Make It Real
### 1. Prerequisites & Environment Setup
#### System Requirements
- **Python 3.11+** (for async support and performance)
- **Node.js 18+** (for MCP servers)
- **Docker & Docker Compose** (for Redis, monitoring stack)
- **GPU (optional)** for local LLMs
- **8GB+ RAM** minimum, 16GB recommended
#### API Keys & Accounts
```bash
# Required API keys (store in .env)
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=... # Optional
HUGGINGFACE_TOKEN=... # For embeddings
# Optional services
LANGSMITH_API_KEY=... # For tracing
OPENROUTER_API_KEY=... # For unified LLM access
```
### 2. Core Dependencies Installation
```bash
# Clone and setup
git clone https://github.com/yourusername/LocalMCP.git
cd LocalMCP
# Python environment
python -m venv venv
source venv/bin/activate # or `venv\Scripts\activate` on Windows
pip install -r requirements.txt
# Node.js dependencies for MCP servers
npm install
# Start infrastructure services
docker-compose up -d
```
### 3. MCP Server Setup
#### Install Official MCP Servers
```bash
# Filesystem MCP server
npx @modelcontextprotocol/create-server filesystem ./mcp_servers/filesystem
# PostgreSQL MCP server
npx @modelcontextprotocol/create-server postgresql ./mcp_servers/database
# Custom tool server template
npx @modelcontextprotocol/create-server custom ./mcp_servers/custom_tools
```
### 4. Vector Database Setup
```bash
# Option 1: Chroma (embedded)
pip install chromadb
# Option 2: Qdrant (more scalable)
docker run -p 6333:6333 qdrant/qdrant
# Option 3: Pinecone (cloud)
pip install pinecone-client
```
### 5. Local LLM Setup (Optional)
```bash
# Option 1: Ollama
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull llama2
ollama pull codellama
# Option 2: LocalAI
curl https://localai.io/install.sh | sh
local-ai run huggingface://TheBloke/CodeLlama-7B-GGUF/codellama-7b.Q4_K_M.gguf
# Option 3: LM Studio GUI
# Download from https://lmstudio.ai/
```
## How LocalMCP Works Modularly with Other Systems
### 1. API-First Architecture
LocalMCP exposes multiple interfaces for integration:
```python
# REST API for external systems
from fastapi import FastAPI
from localmcp import MCPAgent
app = FastAPI()
agent = MCPAgent()
@app.post("/api/v1/execute")
async def execute_command(request: CommandRequest):
"""Execute commands via HTTP API"""
result = await agent.execute(request.command, request.context)
return {"result": result, "tools_used": result.tools}
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
"""Real-time streaming interface"""
await websocket.accept()
async for message in websocket:
result = await agent.stream_execute(message)
await websocket.send_json(result)
```
### 2. Plugin Architecture
```python
# localmcp/plugins/base.py
class MCPPlugin:
"""Base class for LocalMCP plugins"""
async def on_tool_discovery(self, tools: List[Tool]):
"""Called when new tools are discovered"""
pass
async def on_pre_execution(self, tool_call: ToolCall):
"""Called before tool execution"""
pass
async def on_post_execution(self, result: ToolResult):
"""Called after tool execution"""
pass
# Example plugin: Slack integration
class SlackPlugin(MCPPlugin):
async def on_post_execution(self, result: ToolResult):
if result.important:
await self.slack_client.post_message(
channel="#ai-notifications",
text=f"Tool {result.tool_name} completed: {result.summary}"
)
```
### 3. Message Queue Integration
```python
# Celery integration for async task processing
from celery import Celery
from localmcp import MCPOrchestrator
celery_app = Celery('localmcp', broker='redis://localhost:6379')
@celery_app.task
def process_workflow(workflow_definition: dict):
"""Process MCP workflows asynchronously"""
orchestrator = MCPOrchestrator()
return orchestrator.execute_workflow(workflow_definition)
# Kafka integration for event streaming
from aiokafka import AIOKafkaConsumer, AIOKafkaProducer
class MCPEventStreamer:
async def stream_tool_events(self):
producer = AIOKafkaProducer(bootstrap_servers='localhost:9092')
await producer.start()
async for event in self.mcp_agent.event_stream():
await producer.send('mcp-events', event.to_json())
```
### 4. Database Integration
```python
# SQLAlchemy models for storing MCP data
from sqlalchemy import Column, String, JSON, DateTime
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class ToolExecution(Base):
__tablename__ = 'tool_executions'
id = Column(String, primary_key=True)
tool_id = Column(String, index=True)
input_params = Column(JSON)
output_result = Column(JSON)
execution_time = Column(Float)
timestamp = Column(DateTime, default=datetime.utcnow)
# MongoDB integration for unstructured data
from motor.motor_asyncio import AsyncIOMotorClient
class MCPDocumentStore:
def __init__(self):
self.client = AsyncIOMotorClient('mongodb://localhost:27017')
self.db = self.client.localmcp
async def store_conversation(self, conversation: dict):
await self.db.conversations.insert_one(conversation)
```
### 5. Integration Patterns
#### A. Sidecar Pattern
```yaml
# docker-compose.yml
services:
your-app:
image: your-app:latest
localmcp-sidecar:
image: localmcp:latest
environment:
- API_MODE=sidecar
- PARENT_SERVICE=your-app
volumes:
- ./mcp-config:/config
```
#### B. Gateway Pattern
```nginx
# nginx.conf - Route MCP requests through API gateway
location /api/mcp/ {
proxy_pass http://localmcp:8000/;
proxy_set_header X-MCP-Auth $http_authorization;
}
location /api/v1/ {
proxy_pass http://your-main-api:3000/;
}
```
#### C. Event-Driven Integration
```python
# Integrate with existing event systems
class MCPEventBridge:
def __init__(self, event_bus):
self.event_bus = event_bus
self.mcp_agent = MCPAgent()
async def handle_event(self, event):
if event.type == "PROCESS_DOCUMENT":
# Use MCP tools to process
result = await self.mcp_agent.execute(
"extract key information from document",
context={"document_id": event.document_id}
)
# Publish result
await self.event_bus.publish(
"DOCUMENT_PROCESSED",
{"document_id": event.document_id, "extracted": result}
)
```
### 6. Authentication & Authorization
```python
# JWT-based auth for API access
from fastapi import Depends, HTTPException
from jose import JWTError, jwt
class MCPAuthMiddleware:
async def verify_token(self, token: str):
try:
payload = jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
return payload.get("sub")
except JWTError:
raise HTTPException(status_code=401)
async def check_tool_permission(self, user_id: str, tool_id: str):
# Check if user has permission to use specific MCP tool
permissions = await self.get_user_permissions(user_id)
return tool_id in permissions.allowed_tools
```
### 7. Monitoring Integration
```python
# Datadog integration
from datadog import initialize, statsd
class MCPMetricsCollector:
def __init__(self):
initialize(api_key='YOUR_DD_API_KEY')
async def record_tool_execution(self, tool_id: str, duration: float):
statsd.histogram('mcp.tool.execution_time', duration, tags=[f'tool:{tool_id}'])
statsd.increment('mcp.tool.executions', tags=[f'tool:{tool_id}'])
# Grafana Loki for log aggregation
import logging
from python_loki import LoggingHandler
handler = LoggingHandler(
url="http://localhost:3100/loki/api/v1/push",
tags={"application": "localmcp"},
)
logging.getLogger().addHandler(handler)
```
### 8. SDK for Other Languages
```typescript
// TypeScript/JavaScript SDK
import { MCPClient } from '@localmcp/sdk';
const client = new MCPClient({
endpoint: 'http://localhost:8000',
apiKey: process.env.MCP_API_KEY
});
// Execute with streaming
const stream = await client.executeStream({
command: 'analyze this codebase',
context: { path: './src' }
});
for await (const chunk of stream) {
console.log(chunk.tool, chunk.result);
}
```
```go
// Go SDK
package main
import "github.com/localmcp/go-sdk"
client := localmcp.NewClient(
localmcp.WithEndpoint("http://localhost:8000"),
localmcp.WithAPIKey(os.Getenv("MCP_API_KEY")),
)
result, err := client.Execute(ctx, &localmcp.ExecuteRequest{
Command: "generate API documentation",
Context: map[string]interface{}{"path": "./api"},
})
```
## Deployment Options
### 1. Kubernetes Deployment
```yaml
# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: localmcp
spec:
replicas: 3
selector:
matchLabels:
app: localmcp
template:
metadata:
labels:
app: localmcp
spec:
containers:
- name: localmcp
image: localmcp:latest
ports:
- containerPort: 8000
env:
- name: REDIS_URL
value: redis://redis:6379
resources:
requests:
memory: "1Gi"
cpu: "500m"
limits:
memory: "2Gi"
cpu: "1000m"
```
### 2. Serverless Deployment
```python
# AWS Lambda handler
import json
from localmcp import MCPAgent
agent = MCPAgent()
def lambda_handler(event, context):
command = event.get('command')
result = agent.execute_sync(command)
return {
'statusCode': 200,
'body': json.dumps(result)
}
```
### 3. Edge Deployment
```dockerfile
# Lightweight container for edge devices
FROM python:3.11-slim
RUN pip install localmcp-edge
CMD ["localmcp-edge", "--mode", "minimal"]
```
## Next Steps
1. **Start with Core**: Implement the orchestrator and circuit breaker first
2. **Add MCP Servers**: Begin with filesystem and database servers
3. **Integrate LLMs**: Start with one provider, then add others
4. **Build APIs**: Create REST and WebSocket interfaces
5. **Add Monitoring**: Implement metrics and tracing
6. **Create Plugins**: Build integrations for your specific needs
7. **Deploy**: Start with Docker, scale to Kubernetes as needed
The modular architecture ensures you can start small and expand based on your needs!