mcpc copilot-instructions.md

MCPC Development Guide

MCPC lets you build agentic MCP servers by composing existing MCP tools. Think of it as creating AI agents from reusable tool libraries, the agent itself is a MCP tool too.

Rules

NEVER use deno specific APIs in code, use node compatible APIs only.

Core Pattern

Every MCPC server follows the same simple pattern:

const server = await mcpc(
  [{ name: "my-agent", version: "1.0.0" }, {
    capabilities: { tools: {} },
  }],
  [{
    name: "my-agent",
    description: "Agent docs with <tool name='server.tool'/> references",
    deps: { mcpServers: {/* MCP server configs */} },
    options: { mode: "agentic" },
  }],
);
await server.connect(new StdioServerTransport());

That's it. Three parts: server metadata, agent definition(s), transport connection.

Tool Selection with XML Tags

Reference tools in your agent description using XML-like syntax:

description: `
Available tools:
<tool name="desktop-commander.read_file"/>
<tool name="github.__ALL__"/>  // All tools from github server
<tool name="tool1" maxResultLength="2000"/>  // Limit result size
<tool name="sensitive" hide/>  // Hide from public interface
`;

Tool names are always servername.toolname. The framework auto-maps both server.tool and server_tool formats.

MCP Server Dependencies

Support all transport types: stdio, streamable-http, sse.

deps: {
  mcpServers: {
    "desktop-commander": {
      command: "npx",
      args: ["-y", "@wonderwhy-er/desktop-commander@latest"],
      transportType: "stdio"
    },
    "github": {
      transportType: "streamable-http",
      url: "https://api.githubcopilot.com/mcp/",
      headers: { "Authorization": "Bearer ${GITHUB_TOKEN}" }  // Env vars work
    }
  }
}

Execution Modes

• mode: "agentic" (default): Interactive tool calls, agent decides next step
• mode: "agentic_workflow": Structured steps (predefined or generated at runtime)
• sampling: true: Autonomous execution in compatible clients (VS Code, etc.)