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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   # 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
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

   # 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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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:

.. code-block:: python

   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 :doc:`guides/platform-architecture` for system design
- Explore :doc:`examples` for production patterns
- Check the :doc:`API Reference <autoapi/haive/mcp/index>` for detailed APIs
- Join the community for advanced discussions