kilocode-mcp-dev

name	kilocode-mcp-dev
description	MCP server development and integration for PMOVES.AI. Use when building MCP servers, configuring MCP clients, or wiring agent-to-agent communication via Model Context Protocol.
keywords	["mcp","model-context-protocol","integration","agent","server"]
version	1.0.0
category	PMOVES/KiloCode-GLM

KiloCode MCP Development

MCP server development and integration using GLM-5.2 for tool definition, schema validation, and agent wiring.

Purpose

Build, configure, and integrate MCP servers that enable PMOVES agents to interact with external services through well-designed tools.

Capabilities

• 🔧 Build MCP servers (Python FastMCP or Node/TypeScript SDK)
• 🔗 Configure MCP clients in kilo.json and .claude/mcp.json
• 🛡️ Validate tool schemas and permission grants
• 📡 Wire agent-to-agent communication via MCP protocol
• ✅ Test MCP server connectivity and tool execution

Integration Points

• Z.AI MCP Servers: zai-vision (local), zai-web-search/reader/zread (remote)
• PMOVES MCP Servers: pmoves-cipher, tailscale, huggingface, docker
• KiloCode Permission: {server}_{tool} pattern in kilo.json
• Claude Code MCP Config: .claude/mcp.json

Workflow

Step 1: Design the MCP Server

# Python FastMCP pattern
from fastmcp import FastMCP

mcp = FastMMC("pmoves-custom-server")

@mcp.tool()
def get_status(service: str) -> dict:
    """Get health status of a PMOVES service."""
    # Implementation
    return {"service": service, "status": "healthy"}

Step 2: Register in kilo.json

{
  "mcp": {
    "pmoves-custom": {
      "type": "local",
      "command": ["python", "-m", "pmoves.tools.mcp.custom_server"],
      "environment": { "API_KEY": "${API_KEY}" }
    }
  },
  "permission": {
    "pmoves-custom_*": "allow"
  }
}

Step 3: Test Connectivity

# Check MCP server responds
curl -sf http://localhost:8080/healthz

# Verify in KiloCode
# Use /mcps command to list available MCP tools

Trigger Phrases

• "build MCP server"
• "wire MCP integration"
• "configure MCP client"
• "add agent tool"