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Kailash MCP — server/client/tools/resources/auth/transports for AI agent integration.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
Claude Code architecture — artifact design, context, agentic patterns. For CC audit/build.
Conformance Walk — freeze-then-judge on the source→delivered→live axis: one cw_core + Source/Delivered/Live adapter families, coverage vs pass-rate, discrete verdicts. Use for any testable surface.
Kailash security (Python) — validation, secrets, injection, authn/z. Hardcoded secrets BLOCKED.
/onboard procedure: read roster + team-memory + posture + claims + codify lease + rules-changed for a new operator joining a multi-operator COC repo.
/certify procedure: brief → probe → gate at 100%; loops failed questions until pass. NO Claude-assistance during gate phase. Curated bank, not LLM-generated.
/ecosystem-init procedure — write the D6 ecosystem-config, run the disclosure scan before write, establish genesis via runEnrollmentCeremony, scaffold non-Kailash STACK.md.
| name | kailash-mcp |
| description | Kailash MCP — server/client/tools/resources/auth/transports for AI agent integration. |
Production-ready MCP server implementation built into Kailash Core SDK for seamless AI agent integration.
Kailash's MCP module provides:
from kailash_mcp import MCPServer
# Create MCP server
server = MCPServer(name="my-server")
# Register workflow as MCP tool
@server.tool()
def summarize(text: str) -> str:
"""Summarize the given text."""
workflow = create_summary_workflow()
results, run_id = runtime.execute(workflow.build())
return results["summary"]
# Run server (stdio transport by default)
server.run()
The Model Context Protocol enables AI agents to:
MCP supports multiple transport mechanisms:
Tools are type-safe functions exposed to AI agents:
Resources expose data to AI agents:
Use MCP when you need to:
from kailash_mcp import MCPServer
from kailash.workflow.builder import WorkflowBuilder
server = MCPServer(name="workflow-server")
@server.tool()
def process_data(input: str) -> dict:
"""Process data through a workflow."""
workflow = WorkflowBuilder()
# Build workflow
results, run_id = runtime.execute(workflow.build())
return results["output"]
from nexus import Nexus
# Nexus automatically creates MCP channel
app = Nexus()
app.register("my_workflow", workflow.build())
app.start() # Includes MCP server
from kailash_mcp import MCPServer
from dataflow import DataFlow
server = MCPServer(name="db-server")
db = DataFlow(...)
@server.resource("users://list")
def get_users():
"""Expose database via MCP resource."""
return db.query_users()
from kailash_mcp import MCPServer
from kaizen.core.base_agent import BaseAgent
server = MCPServer(name="agent-server")
@server.tool()
def analyze(text: str) -> str:
"""Analyze text using an AI agent."""
agent = AnalysisAgent()
return agent(text=text).result
| Transport | Use Case | Pros | Cons |
|---|---|---|---|
| stdio | Local tools, CLI | Simple, reliable | Local only |
| SSE | Web apps | Real-time updates | Complex setup |
| HTTP | APIs, services | Standard protocol | No streaming |
For MCP-specific questions, invoke:
mcp-specialist - MCP server implementationtesting-specialist - MCP testing strategies skill - MCP integration architecture