Advanced Topics¶
Advanced patterns and techniques for haive-mcp with 1900+ MCP servers.
Custom Agent Development¶
Creating Specialized MCP Agents¶
Extend the base MCP agents for domain-specific needs:
from haive.mcp.agents.mcp_agent import MCPAgent
from haive.mcp.config import MCPConfig, MCPServerConfig
class DataScienceMCPAgent(MCPAgent):
"""Specialized agent for data science workflows."""
def __init__(self, **kwargs):
# Pre-configure with data science servers
mcp_config = MCPConfig(
enabled=True,
auto_discover=True,
categories=["data_analysis", "visualization", "ml_tools"]
)
super().__init__(mcp_config=mcp_config, **kwargs)
async def analyze_dataset(self, file_path: str):
"""Analyze a dataset with automatic tool discovery."""
prompt = f"Analyze the dataset at {file_path}, create visualizations, and provide insights"
return await self.arun(prompt)
Dynamic Tool Composition¶
Runtime Tool Discovery¶
Discover and add tools based on task requirements:
from haive.mcp.discovery import MCPDiscoveryEngine
class AdaptiveMCPAgent(EnhancedMCPAgent):
"""Agent that adapts its toolset based on the task."""
async def adapt_to_task(self, task_description: str):
"""Analyze task and add relevant tools."""
# Use discovery engine to find relevant servers
discovery = MCPDiscoveryEngine()
relevant_servers = await discovery.find_servers_for_task(task_description)
# Add servers dynamically
for server in relevant_servers:
await self.add_mcp_server(server)
# Re-initialize with new tools
await self.initialize_mcp()
Server Development¶
Creating Custom MCP Servers¶
Build your own MCP servers for proprietary tools:
# custom_server.py
from mcp import Server, Tool
import asyncio
class CustomMCPServer(Server):
"""Custom MCP server for specialized tools."""
def __init__(self):
super().__init__("custom-server")
self.register_tool(self.custom_operation)
@Tool(
name="custom_operation",
description="Perform custom operation",
parameters={
"type": "object",
"properties": {
"input": {"type": "string"}
}
}
)
async def custom_operation(self, input: str) -> str:
"""Custom tool implementation."""
# Your custom logic here
return f"Processed: {input}"
# Run the server
if __name__ == "__main__":
server = CustomMCPServer()
asyncio.run(server.run())
Integrating Custom Servers¶
# Add custom server to agent
agent = EnhancedMCPAgent(
name="custom_agent",
engine=AugLLMConfig(),
custom_servers=[
{
"name": "custom-server",
"command": "python",
"args": ["custom_server.py"],
"transport": "stdio"
}
],
auto_install=True
)
Performance Optimization¶
Connection Pooling¶
Optimize MCP server connections:
from haive.mcp.optimization import MCPConnectionPool
# Create connection pool
pool = MCPConnectionPool(
max_connections=10,
connection_timeout=30,
idle_timeout=300
)
# Use with agent
agent = EnhancedMCPAgent(
name="optimized",
engine=AugLLMConfig(),
connection_pool=pool,
mcp_categories=["core"]
)
Caching Strategies¶
Implement caching for frequently used operations:
from haive.mcp.cache import MCPCache
# Configure caching
cache = MCPCache(
max_size=1000,
ttl=3600, # 1 hour
cache_tool_results=True
)
agent = EnhancedMCPAgent(
name="cached",
engine=AugLLMConfig(),
cache=cache,
mcp_categories=["core"]
)
Parallel Processing¶
Execute multiple MCP operations in parallel:
import asyncio
async def parallel_mcp_operations():
"""Run multiple MCP operations concurrently."""
agent = EnhancedMCPAgent(
name="parallel",
engine=AugLLMConfig(),
mcp_categories=["core"],
auto_install=True
)
await agent.initialize_mcp()
# Define tasks
tasks = [
agent.arun("Analyze file1.txt"),
agent.arun("Search for patterns in logs"),
agent.arun("Generate report from data")
]
# Execute in parallel
results = await asyncio.gather(*tasks)
return results
Security Considerations¶
Sandboxing MCP Servers¶
Run MCP servers in isolated environments:
from haive.mcp.security import MCPSandbox
# Configure sandbox
sandbox = MCPSandbox(
enable_network=False,
filesystem_access=["/tmp", "/data"],
memory_limit="512M",
cpu_limit=0.5
)
agent = EnhancedMCPAgent(
name="secure",
engine=AugLLMConfig(),
sandbox=sandbox,
mcp_categories=["core"]
)
Access Control¶
Implement fine-grained access control:
from haive.mcp.security import MCPAccessControl
# Define access policies
access_control = MCPAccessControl()
access_control.add_policy(
server="filesystem",
allowed_operations=["read"],
denied_paths=["/etc", "/var"]
)
agent = EnhancedMCPAgent(
name="restricted",
engine=AugLLMConfig(),
access_control=access_control,
mcp_categories=["core"]
)
Monitoring & Observability¶
Metrics Collection¶
Track MCP usage and performance:
from haive.mcp.monitoring import MCPMetrics
# Setup metrics
metrics = MCPMetrics(
enable_prometheus=True,
port=9090
)
agent = EnhancedMCPAgent(
name="monitored",
engine=AugLLMConfig(),
metrics=metrics,
mcp_categories=["core"]
)
# Access metrics
stats = metrics.get_statistics()
print(f"Total operations: {stats['total_operations']}")
print(f"Average latency: {stats['avg_latency_ms']}ms")
Distributed Tracing¶
Trace MCP operations across systems:
from haive.mcp.tracing import MCPTracer
from opentelemetry import trace
# Configure tracing
tracer = MCPTracer(
service_name="mcp-agent",
jaeger_endpoint="http://localhost:14268"
)
agent = EnhancedMCPAgent(
name="traced",
engine=AugLLMConfig(),
tracer=tracer,
mcp_categories=["core"]
)
Scaling Strategies¶
Horizontal Scaling¶
Distribute MCP operations across multiple instances:
from haive.mcp.scaling import MCPCluster
# Create MCP cluster
cluster = MCPCluster(
nodes=["node1:8080", "node2:8080", "node3:8080"],
load_balancer="round_robin"
)
agent = EnhancedMCPAgent(
name="distributed",
engine=AugLLMConfig(),
cluster=cluster,
mcp_categories=["core"]
)
Queue-Based Processing¶
Process MCP requests through queues:
from haive.mcp.queue import MCPQueue
import asyncio
# Setup queue
queue = MCPQueue(
broker="redis://localhost:6379",
max_workers=10
)
# Producer
async def produce_tasks():
agent = EnhancedMCPAgent(
name="producer",
engine=AugLLMConfig(),
queue=queue,
mcp_categories=["core"]
)
# Queue tasks
for i in range(100):
await agent.queue_task(f"Process file_{i}.txt")
# Consumer
async def consume_tasks():
await queue.start_workers()
Next Steps¶
Review Platform Architecture for system design
Explore Examples for production patterns
Check the API Reference for detailed APIs
Join the community for advanced discussions