Cursor + MCP + 微调模型：在你的代码编辑器中使用领域 AI

// cursor-domain-mcp/server.mjs
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js';

const OLLAMA_URL = 'http://localhost:11434/api/chat';
const MODEL = 'my-codebase-model';

const server = new Server(
  { name: 'codebase-assistant', version: '1.0.0' },
  { capabilities: { tools: {} } }
);

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: 'generate_code',
      description: 'Generate code following the project\'s specific patterns, naming conventions, and library choices. Use for creating new functions, components, or modules that should match existing code style.',
      inputSchema: {
        type: 'object',
        properties: {
          description: { type: 'string', description: 'What code to generate' },
          file_context: { type: 'string', description: 'Relevant existing code from the codebase for context' },
          language: { type: 'string', description: 'Programming language or framework' }
        },
        required: ['description']
      }
    },
    {
      name: 'write_documentation',
      description: 'Write documentation in the project\'s established style — same format, same level of detail, same terminology. Use for JSDoc, README sections, or inline comments.',
      inputSchema: {
        type: 'object',
        properties: {
          code: { type: 'string', description: 'The code to document' },
          doc_type: { type: 'string', description: 'jsdoc, readme, comment, or api-reference' }
        },
        required: ['code']
      }
    },
    {
      name: 'domain_query',
      description: 'Answer domain-specific questions about the business logic, industry rules, or specialized requirements that the codebase implements.',
      inputSchema: {
        type: 'object',
        properties: {
          question: { type: 'string', description: 'The domain question to answer' }
        },
        required: ['question']
      }
    }
  ]
}));

server.setRequestHandler(CallToolRequestSchema, async (request) => {
  const { name, arguments: args } = request.params;

  let prompt;
  if (name === 'generate_code') {
    prompt = `Generate code for: ${args.description}
${args.file_context ? `\nExisting code context:\n${args.file_context}` : ''}
${args.language ? `\nLanguage/Framework: ${args.language}` : ''}`;
  } else if (name === 'write_documentation') {
    prompt = `Write ${args.doc_type || 'documentation'} for this code:\n\n${args.code}`;
  } else if (name === 'domain_query') {
    prompt = args.question;
  } else {
    throw new Error(`Unknown tool: ${name}`);
  }

  const response = await fetch(OLLAMA_URL, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      model: MODEL,
      messages: [{ role: 'user', content: prompt }],
      stream: false
    })
  });

  const data = await response.json();
  return { content: [{ type: 'text', text: data.message.content }] };
});

const transport = new StdioServerTransport();
await server.connect(transport);

{
  "mcpServers": {
    "codebase-assistant": {
      "command": "node",
      "args": ["/absolute/path/to/cursor-domain-mcp/server.mjs"]
    }
  }
}