Kailash MCP - Model Context Protocol Integration

Production-ready MCP server implementation built into Kailash Core SDK for seamless AI agent integration.

Overview

Kailash's MCP module provides:

• Full MCP Specification: Complete implementation of Model Context Protocol
• Multiple Transports: stdio, SSE, HTTP support
• Structured Tools: Type-safe tool definitions
• Resource Management: Expose data sources to AI agents
• Authentication: Secure MCP server access
• Progress Reporting: Real-time operation status
• Testing Support: Comprehensive testing utilities

Quick Start

from kailash.mcp_server import MCPServer

# Create MCP server
server = MCPServer(name="my-server")

# Register workflow as MCP tool
@server.tool("summarize")
def summarize_tool(text: str) -> str:
    """Summarize the given text."""
    # Execute workflow
    workflow = create_summary_workflow()
    results = runtime.execute(workflow.build())
    return results["summary"]["result"]

# Run server (stdio transport by default)
server.run()

Reference Documentation

Getting Started

• mcp-transports-quick - Transport configuration (stdio, SSE, HTTP)
• mcp-structured-tools - Defining MCP tools
• mcp-resources - Exposing resources to agents

Security & Operations

• mcp-authentication - Authentication and authorization
• mcp-progress-reporting - Progress updates for long operations
• mcp-testing-patterns - Testing MCP servers and tools

Key Concepts

MCP Protocol

The Model Context Protocol enables AI agents to:

• Tools: Call structured functions
• Resources: Access data sources
• Prompts: Use pre-defined templates
• Sampling: Request LLM completions

Transports

MCP supports multiple transport mechanisms:

• stdio: Standard input/output (default, simplest)
• SSE: Server-Sent Events (for web clients)
• HTTP: RESTful API (for HTTP clients)

Structured Tools

Tools are type-safe functions exposed to AI agents:

• Automatic schema generation from Python type hints
• Input validation
• Error handling
• Progress reporting

Resources

Resources expose data to AI agents:

• File systems
• Databases
• APIs
• Custom data sources

When to Use This Skill

Use MCP when you need to:

• Expose workflows as tools for AI agents
• Build MCP servers for Claude Desktop or other clients
• Integrate Kailash workflows with AI assistants
• Provide structured tools to language models
• Expose resources for RAG applications
• Build custom MCP integrations

Integration Patterns

With Core SDK (Workflow Tools)

from kailash.mcp_server import MCPServer
from kailash.workflow.builder import WorkflowBuilder

server = MCPServer(name="workflow-server")

@server.tool("process_data")
def process_tool(input: str) -> dict:
    workflow = WorkflowBuilder()
    # Build workflow
    results = runtime.execute(workflow.build())
    return results["output"]["result"]

With Nexus (Multi-Channel with MCP)

from nexus import Nexus

# Nexus automatically creates MCP channel
nexus = Nexus(workflows)
nexus.run()  # Includes MCP server

With DataFlow (Database Access)

from kailash.mcp_server import MCPServer
from dataflow import DataFlow

server = MCPServer(name="db-server")
db = DataFlow(...)

@server.resource("users")
def get_users():
    # Expose database via MCP resource
    return db.query_users()

With Kaizen (Agent Tools)

from kailash.mcp_server import MCPServer
from kaizen.base import BaseAgent

server = MCPServer(name="agent-server")

@server.tool("analyze")
def analyze_tool(text: str) -> str:
    agent = AnalysisAgent()
    return agent(text=text).result

Critical Rules

• ✅ Use stdio transport for local development
• ✅ Define clear tool schemas with type hints
• ✅ Implement progress reporting for long operations
• ✅ Test MCP servers with real MCP clients
• ✅ Use authentication for production servers
• ❌ NEVER expose sensitive data without authentication
• ❌ NEVER skip input validation
• ❌ NEVER mock MCP protocol in tests (use real transports)

Transport Selection

Version Compatibility

• Core SDK Version: 0.9.25+
• MCP Specification: Latest
• Python: 3.8+
• Transports: stdio, SSE, HTTP

Related Skills

• 01-core-sdk - Core workflow patterns
• 03-nexus - Nexus includes MCP channel
• 04-kaizen - AI agents as MCP tools
• 02-dataflow - Database resources

Support

For MCP-specific questions, invoke:

• mcp-specialist - MCP server implementation
• testing-specialist - MCP testing strategies
• framework-advisor - MCP integration architecture