Manage MCP

Complete skill for managing Model Context Protocol (MCP) servers in Nuxt applications. Setup, create, customize with middleware and handlers, review, and troubleshoot.

When to Use

• Setup: "Setup an MCP server in my Nuxt app"
• Create: "Create a tool to calculate BMI" / "Add a resource to read the README"
• Customize: "Add authentication to my MCP server" / "Create middleware for rate limiting"
• Review: "Review my MCP implementation" / "Check for best practices"
• Troubleshoot: "My auto-imports aren't working" / "Cannot connect to endpoint"
• Test: "Create tests for my MCP tools"

---

Setup MCP Server

Installation

Automatic (recommended):

npx nuxt module add mcp-toolkit

Manual:

pnpm add -D @nuxtjs/mcp-toolkit zod

Add to nuxt.config.ts:

export default defineNuxtConfig({
  modules: ['@nuxtjs/mcp-toolkit'],
  mcp: {
    name: 'My MCP Server',
  },
})

Directory Structure

server/mcp/
├── tools/       # Actions AI can perform
├── resources/   # Data AI can read
└── prompts/     # Message templates

Verification

1. Start: pnpm dev
2. Check: http://localhost:3000/mcp (should redirect)
3. Open DevTools (Shift+Alt+D) → MCP tab

---

Create Tools

Tools are functions AI assistants can call.

Basic Structure

import { z } from 'zod'

export default defineMcpTool({
  description: 'What the tool does',
  inputSchema: {
    param: z.string().describe('Parameter description'),
  },
  handler: async ({ param }) => {
    return {
      content: [{
        type: 'text',
        text: 'Result',
      }],
    }
  },
})

Input Patterns

// Required
name: z.string().describe('User name')

// Optional with default
limit: z.number().default(10).describe('Max results')

// Enum
format: z.enum(['json', 'xml']).describe('Format')

// Array
tags: z.array(z.string()).describe('Tags')

Error Handling

if (!param) {
  return {
    content: [{ type: 'text', text: 'Error: param required' }],
    isError: true,
  }
}

Annotations

Behavioral hints that help MCP clients decide when to prompt for confirmation:

export default defineMcpTool({
  annotations: {
    readOnlyHint: true,     // Only reads data, no side effects
    destructiveHint: false,  // Does not delete or destroy data
    idempotentHint: false,   // Multiple calls may have different effects
    openWorldHint: false,    // No external API calls
  },
  // ...
})

Common patterns: read-only tools → readOnlyHint: true, create → idempotentHint: false, update → idempotentHint: true, delete → destructiveHint: true, idempotentHint: true.

Input Examples

Type-safe usage examples that help AI models fill in parameters correctly:

export default defineMcpTool({
  inputSchema: {
    title: z.string().describe('Todo title'),
    content: z.string().optional().describe('Description'),
  },
  inputExamples: [
    { title: 'Buy groceries', content: 'Milk, eggs, bread' },
    { title: 'Fix login bug' },
  ],
  // ...
})

Caching

export default defineMcpTool({
  cache: '5m',  // 5 minutes
  // ...
})

See detailed examples →

---

Create Resources

Resources expose read-only data.

File Resource

import { readFile } from 'node:fs/promises'

export default defineMcpResource({
  description: 'Read a file',
  uri: 'file:///README.md',
  mimeType: 'text/markdown',
  handler: async (uri: URL) => {
    const content = await readFile('README.md', 'utf-8')
    return {
      contents: [{
        uri: uri.toString(),
        text: content,
        mimeType: 'text/markdown',
      }],
    }
  },
})

API Resource

export default defineMcpResource({
  description: 'Fetch API data',
  uri: 'api:///users',
  mimeType: 'application/json',
  cache: '5m',
  handler: async (uri: URL) => {
    const data = await $fetch('https://api.example.com/users')
    return {
      contents: [{
        uri: uri.toString(),
        text: JSON.stringify(data, null, 2),
        mimeType: 'application/json',
      }],
    }
  },
})

Dynamic Resource

import { z } from 'zod'

export default defineMcpResource({
  description: 'Fetch by ID',
  uriTemplate: {
    uriTemplate: 'user:///{id}',
    arguments: {
      id: z.string().describe('User ID'),
    },
  },
  handler: async (uri: URL, args) => {
    const user = await fetchUser(args.id)
    return {
      contents: [{
        uri: uri.toString(),
        text: JSON.stringify(user),
        mimeType: 'application/json',
      }],
    }
  },
})

See detailed examples →

---

Create Prompts

Prompts are reusable message templates.

Static Prompt

export default defineMcpPrompt({
  description: 'Code review',
  handler: async () => {
    return {
      messages: [{
        role: 'user',
        content: {
          type: 'text',
          text: 'Review this code for best practices.',
        },
      }],
    }
  },
})

Dynamic Prompt

import { z } from 'zod'

export default defineMcpPrompt({
  description: 'Custom review',
  inputSchema: {
    language: z.string().describe('Language'),
    focus: z.array(z.string()).describe('Focus areas'),
  },
  handler: async ({ language, focus }) => {
    return {
      messages: [{
        role: 'user',
        content: {
          type: 'text',
          text: `Review my ${language} code: ${focus.join(', ')}`,
        },
      }],
    }
  },
})

See detailed examples →

---

Middleware & Handlers

Customize MCP behavior with middleware and handlers for authentication, logging, rate limiting, and more.

Basic Middleware

// server/mcp/middleware.ts
export default defineMcpMiddleware({
  handler: async (event, next) => {
    console.log('MCP Request:', event.path)

    // Check auth
    const token = event.headers.get('authorization')
    if (!token) {
      return createError({ statusCode: 401, message: 'Unauthorized' })
    }

    return next()
  },
})

Custom Handler

// server/mcp/handlers/custom.ts
export default defineMcpHandler({
  name: 'custom-mcp',
  route: '/mcp/custom',
  handler: async (event) => {
    return {
      tools: await loadCustomTools(),
      resources: [],
      prompts: [],
    }
  },
})

Common Use Cases

• Authentication: API keys, JWT tokens
• Rate limiting: Per IP or per user
• Logging: Request/response tracking
• CORS: Cross-origin configuration
• Multiple endpoints: Public/admin separation

See detailed middleware guide →

---

Review & Best Practices

Tool Checklist

✅ Use kebab-case filenames ✅ Add .describe() to all Zod fields ✅ Return isError: true for errors ✅ Add caching for expensive ops ✅ Clear, actionable descriptions ✅ Validate all inputs ✅ Add annotations (readOnlyHint, destructiveHint, etc.) ✅ Add inputExamples for tools with optional/complex params

❌ Generic descriptions ❌ Skip error handling ❌ Expose sensitive data ❌ No input validation

Resource Checklist

✅ Descriptive URIs (config:///app) ✅ Set appropriate MIME types ✅ Enable caching when needed ✅ Handle errors gracefully ✅ Use URI templates for collections

❌ Unclear URI schemes ❌ Skip MIME types ❌ Expose sensitive data ❌ Return huge datasets without pagination

Prompt Checklist

✅ Clear descriptions ✅ Meaningful parameters ✅ Default values where appropriate ✅ Single, focused purpose ✅ Reusable design

❌ Overly complex ❌ Skip descriptions ❌ Mix multiple concerns

---

Troubleshooting

Auto-imports Not Working

Fix:

1. Check modules: ['@nuxtjs/mcp-toolkit'] in config
2. Restart dev server
3. Files in server/mcp/ directory?
4. Run pnpm nuxt prepare

Endpoint Not Accessible

Fix:

1. Dev server running?
2. Test: curl http://localhost:3000/mcp
3. Check enabled: true in config
4. Review server logs

Validation Errors

Fix:

• Required fields provided?
• Types match schema?
• Use .optional() for optional fields
• Enum values exact match?

Tool Not Discovered

Fix:

• File extension .ts or .js?
• Using export default?
• File in correct directory?
• Restart dev server

See detailed troubleshooting →

---

Testing with Evals

Setup

pnpm add -D evalite vitest @ai-sdk/mcp ai

Add to package.json:

{
  "scripts": {
    "eval": "evalite",
    "eval:ui": "evalite watch"
  }
}

Basic Test

Create test/mcp.eval.ts:

import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp'
import { generateText } from 'ai'
import { evalite } from 'evalite'
import { toolCallAccuracy } from 'evalite/scorers'

evalite('MCP Tool Selection', {
  data: async () => [
    {
      input: 'Calculate BMI for 70kg 1.75m',
      expected: [{
        toolName: 'bmi-calculator',
        input: { weight: 70, height: 1.75 },
      }],
    },
  ],
  task: async (input) => {
    const mcp = await createMCPClient({
      transport: { type: 'http', url: 'http://localhost:3000/mcp' },
    })
    try {
      const result = await generateText({
        model: 'openai/gpt-4o',
        prompt: input,
        tools: await mcp.tools(),
      })
      return result.toolCalls ?? []
    }
    finally {
      await mcp.close()
    }
  },
  scorers: [
    ({ output, expected }) => toolCallAccuracy({
      actualCalls: output,
      expectedCalls: expected,
    }),
  ],
})

Running

# Start server
pnpm dev

# Run tests (in another terminal)
pnpm eval

# Or with UI
pnpm eval:ui  # http://localhost:3006

See detailed testing guide →

---

Quick Reference

Common Commands

# Setup
npx nuxt module add mcp-toolkit

# Dev
pnpm dev

# Test endpoint
curl http://localhost:3000/mcp

# Regenerate types
pnpm nuxt prepare

# Run evals
pnpm eval

Configuration

// nuxt.config.ts
export default defineNuxtConfig({
  mcp: {
    name: 'My Server',
    route: '/mcp',
    enabled: true,
    dir: 'mcp',
  },
})

Debug Tools

• DevTools: Shift+Alt+D → MCP tab
• Logs: Check terminal
• curl: Test endpoint

Learn More

• Documentation
• Tools Guide
• Resources Guide
• Prompts Guide
• MCP Protocol