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MCP Provider Integration Guide

Overview

Task Master provides a unified MCP provider for AI operations:

MCP Provider (mcp) - Modern AI SDK-compatible provider with full structured object generation support

The MCP provider enables Task Master to act as an MCP client, using MCP servers as AI providers alongside traditional API-based providers. This integration follows the existing provider pattern and supports all standard AI operations including structured object generation for PRD parsing and task creation.

MCP Provider Features

The MCP Provider (mcp) provides:

✅ Full AI SDK Compatibility - Complete LanguageModelV1 interface implementation
✅ Structured Object Generation - Schema-driven outputs for PRD parsing and task creation
✅ Enhanced Error Handling - Robust JSON extraction and validation
✅ Session Management - Automatic session detection and context handling
✅ Schema Validation - Type-safe object generation with Zod validation

Quick Setup

# Set MCP provider for main role  
task-master models set-main --provider mcp --model claude-3-5-sonnet-20241022

For detailed information, see MCP Provider Documentation.

What is MCP Provider?

The MCP provider allows Task Master to:

Connect to MCP servers/tools as AI providers
Use session-based authentication instead of API keys
Map AI operations to MCP tool calls
Integrate with existing role-based provider assignment
Maintain compatibility with fallback chains
Support structured object generation for schema-driven features

Configuration

MCP Provider Setup

Add MCP provider to your .taskmaster/config.json:

{
  "models": {
    "main": {
      "provider": "mcp",
      "modelId": "claude-3-5-sonnet-20241022",
      "maxTokens": 50000,
      "temperature": 0.2
    },
    "research": {
      "provider": "mcp", 
      "modelId": "claude-3-5-sonnet-20241022",
      "maxTokens": 8700,
      "temperature": 0.1
    },
    "fallback": {
      "provider": "anthropic",
      "modelId": "claude-3-5-sonnet-20241022"
    }
  }
}

Available Models

MCP Provider Models:

claude-3-5-sonnet-20241022 - High-performance model for general tasks
- SWE Score: 0.49
- Features: Text + Object generation
claude-3-opus-20240229 - Enhanced reasoning model for complex tasks
- SWE Score: 0.725
- Features: Text + Object generation
mcp-sampling - General text generation using MCP client sampling
- SWE Score: null
- Roles: Supports main, research, and fallback roles
- SWE Score: 0.49
- Cost: $0 (session-based)
- Max Tokens: 200,000
- Supported Roles: main, research, fallback
- Features: Text + Object generation
claude-3-opus-20240229 - Enhanced reasoning model for complex tasks
- SWE Score: 0.725
- Cost: $0 (session-based)
- Max Tokens: 200,000
- Supported Roles: main, research, fallback
- Features: Text + Object generation

Basic MCP Provider Models:

mcp-sampling - General text generation using MCP client sampling
mcp-sampling - General text generation using MCP client sampling
- SWE Score: null
- Roles: Supports main, research, and fallback roles

Model ID Format

MCP model IDs use a simple format:

claude-3-5-sonnet-20241022 - Uses Claude 3.5 Sonnet via MCP sampling
claude-3-opus-20240229 - Uses Claude 3 Opus via MCP sampling
mcp-sampling - Uses MCP client's sampling capability for text generation

Session Requirements

The MCP provider requires an active MCP session with sampling capabilities:

session: {
  clientCapabilities: {
    sampling: {} // Client supports sampling requests
  }
}

Usage Examples

Basic Text Generation

import { generateTextService } from './scripts/modules/ai-services-unified.js';

const result = await generateTextService({
  role: 'main',
  session: mcpSession, // Required for MCP provider
  prompt: 'Explain MCP integration',
  systemPrompt: 'You are a helpful AI assistant'
});

console.log(result.text);

Structured Object Generation

import { generateObjectService } from './scripts/modules/ai-services-unified.js';

const result = await generateObjectService({
  role: 'main',
  session: mcpSession,
  prompt: 'Create a task breakdown',
  schema: {
    type: 'object',
    properties: {
      tasks: {
        type: 'array',
        items: { type: 'string' }
      }
    }
  }
});

console.log(result.object);

Research Operations

const research = await generateTextService({
  role: 'research',
  session: mcpSession,
  prompt: 'Research the latest developments in AI',
  systemPrompt: 'You are a research assistant'
});

CLI Integration

The MCP provider works seamlessly with Task Master CLI commands when running in an MCP context:

# Generate tasks using MCP provider (if configured as main)
task-master add-task "Implement user authentication"

# Research using MCP provider (if configured as research)
task-master research "OAuth 2.0 best practices"

# Parse PRD using MCP provider
task-master parse-prd requirements.txt

Architecture Details

Provider Architecture

MCPProvider (mcp-server/src/providers/mcp-provider.js)

Modern AI SDK-compliant provider for Task Master's MCP server
Auto-registers when MCP sessions connect to Task Master
Enables Task Master to use MCP sessions for AI operations
Supports both text generation and structured object generation

Auto-Registration Process

When running as an MCP server, Task Master automatically:

// On MCP session connect
server.on("connect", (event) => {
  // Check session capabilities
  if (session.clientCapabilities?.sampling) {
    // Create and register MCP provider
    const mcpProvider = new MCPProvider();
    mcpProvider.setSession(session);
    
    // Auto-register with provider registry
    providerRegistry.registerProvider('mcp', mcpProvider);
  }
});

This enables seamless self-referential AI operations within MCP contexts.

Provider Pattern Integration

The MCP provider follows the same pattern as other providers:

class MCPProvider extends BaseAIProvider {
  // Implements generateText, generateObject
  // Uses session context instead of API keys
  // Maps operations to MCP sampling requests
}

Session Detection

The provider automatically detects MCP sampling capability when sessions connect:

// On MCP session connect
if (session.clientCapabilities?.sampling) {
  // Auto-register MCP provider for use
  const mcpProvider = new MCPProvider();
  mcpProvider.setSession(session);
}

Sampling Integration

AI operations use MCP sampling with different levels of support:

generateText() → MCP requestSampling() with messages (2-minute timeout) ✅ Full Support
streamText() → Limited/No Support ⚠️ See streaming limitations below
generateObject() → MCP requestSampling() with JSON schema instructions (2-minute timeout) ✅ Full Support

Timeout Configuration: All MCP sampling requests use a 2-minute (120,000ms) timeout to accommodate complex AI operations.

Streaming Text Limitations ⚠️

Important: The MCP provider has no support for text streaming:

MCPProvider:

❌ No Streaming Support: Throws error "MCP Provider does not support streaming text, use generateText instead"
Solution: Always use generateText() instead of streamText() with this provider

Recommendation: For streaming functionality, configure a non-MCP fallback provider (like Anthropic or OpenAI) in your fallback role.

Error Handling

The MCP provider includes comprehensive error handling:

Session validation errors (checks for clientCapabilities.sampling)
MCP sampling request failures
JSON parsing errors (for structured output)
Automatic fallback to other providers

Best Practices

1. Configure Fallbacks

Always configure a non-MCP fallback provider, especially for streaming operations:

{
  "models": {
    "main": {
      "provider": "mcp",
      "modelId": "mcp-sampling"
    },
    "fallback": {
      "provider": "anthropic",
      "modelId": "claude-3-5-sonnet-20241022"
    }
  }
}

2. Avoid Streaming with MCP

Do not use streamTextService() with MCP provider. Use generateTextService() instead:

// ❌ Don't do this with MCP provider
const result = await streamTextService({
  role: 'main', // MCP provider
  session: mcpSession,
  prompt: 'Generate content'
});

// ✅ Do this instead
const result = await generateTextService({
  role: 'main', // MCP provider
  session: mcpSession,
  prompt: 'Generate content'
});

3. Session Management

Ensure your MCP session remains active throughout Task Master operations:

// Check session health before operations
if (!session || !session.capabilities) {
  throw new Error('MCP session not available');
}

4. Tool Availability

Verify required capabilities are available in your MCP session:

// Check session health and capabilities
if (session && session.clientCapabilities && session.clientCapabilities.sampling) {
  console.log('MCP sampling available');
} else {
  console.log('MCP sampling not available');
}

5. Error Recovery

Handle MCP-specific errors gracefully:

try {
  const result = await generateTextService({
    role: 'main',
    session: mcpSession,
    prompt: 'Generate content'
  });
} catch (error) {
  if (error.message.includes('MCP')) {
    // Handle MCP-specific error
    console.log('MCP error, falling back to alternate provider');
  }
}

Troubleshooting

Common Issues

"MCP provider requires session context"
- Ensure session parameter is passed to service calls
- Verify session has proper structure
- Check that you're running in an MCP environment
"MCP session must have client sampling capabilities"
- Check that session.clientCapabilities.sampling exists
- Verify session has requestSampling() method
- Ensure MCP client supports sampling feature
"MCP Provider does not support streaming text, use generateText instead"
- Common Error: Occurs when calling streamTextService() with MCP provider
- Solution: Use generateTextService() instead of streamTextService()
- Alternative: Configure a non-MCP fallback provider for streaming operations
"MCP sampling failed" or Timeout errors
- Check MCP client is responding to sampling requests
- Verify session is still active and connected
- Consider if request complexity requires longer processing time
- Check for network connectivity issues
"Model ID is required for MCP Remote Provider"
- Ensure modelId is specified in configuration
- Use mcp-sampling as the standard model ID
- Verify provider configuration is properly loaded
Auto-registration failures
- Check that MCP session has required sampling capabilities
- Verify server event listeners are properly configured
- Look for provider registry initialization issues

Error: streamTextService() calls fail with MCP provider Cause: MCP provider has no streaming support Solutions:

Use generateTextService() for all MCP-based text generation
Configure non-MCP fallback providers for streaming requirements
Check your provider configuration to ensure fallback chain includes streaming-capable providers

Debug Mode

Enable debug logging to see MCP provider operations:

// Set debug flag in config or environment
process.env.DEBUG = 'true';

// Or in .taskmasterconfig
{
  "debug": true,
  "models": { /* ... */ }
}

Testing MCP Integration

Test MCP provider functionality:

// Check if MCP provider is properly registered
import { MCPProvider } from './mcp-server/src/providers/mcp-provider.js';

// Test session capabilities
if (session && session.clientCapabilities && session.clientCapabilities.sampling) {
  console.log('MCP sampling available');
  
  // Test provider creation
  const provider = new MCPProvider();
  provider.setSession(session);
  console.log('MCP provider created successfully');
} else {
  console.log('MCP session lacks required capabilities');
}

Integration with Development Tools

VS Code with MCP Extension

When using Task Master in VS Code with MCP support:

Configure Task Master MCP server in your .vscode/mcp.json
Set MCP provider as main/research in .taskmaster/config.json
Benefit from integrated AI assistance within your development workflow
Use Task Master tools directly from VS Code's MCP interface

Example VS Code MCP Configuration:

{
  "servers": {
    "task-master-dev": {
      "command": "npx",
      "args": ["-y", "task-master-ai"],
      "cwd": "/path/to/your/task-master-project",
      "env": {
        "NODE_ENV": "development",
        "ANTHROPIC_API_KEY": "${env:ANTHROPIC_API_KEY}",
        "TASK_MASTER_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

Claude Desktop

When using Task Master through Claude Desktop's MCP integration:

Configure Task Master as MCP provider in Claude Desktop
Use MCP provider for AI operations within Task Master
Benefit from nested MCP tool calling capabilities

Cursor and Other MCP Clients

The MCP provider works with any MCP-compatible development environment:

Ensure your IDE has MCP client capabilities
Configure Task Master MCP server endpoint
Use MCP provider for enhanced AI-driven development

Advanced Configuration

Custom Tool Mapping

Advanced users can use MCP sampling for all roles:

// MCP sampling for all roles
{
  "models": {
    "main": {
      "provider": "mcp",
      "modelId": "mcp-sampling"
    }
  }
}

Role-Specific Configuration

Configure MCP sampling for different roles:

{
  "models": {
    "main": {
      "provider": "mcp",
      "modelId": "mcp-sampling"
    },
    "research": {
      "provider": "mcp", 
      "modelId": "mcp-sampling"
    },
    "fallback": {
      "provider": "mcp",
      "modelId": "backup-server:simple-generation"
    }
  }
}

API Reference

MCPProvider Methods

generateText(params) - Generate text using MCP sampling ✅ Supported
streamText(params) - Stream text ❌ Not supported (throws error)
generateObject(params) - Generate structured objects ✅ Supported
setSession(session) - Update provider session
validateAuth(params) - Validate session capabilities
getClient() - Returns null (not applicable for MCP)

Required Parameters

All MCP operations require:

session - Active MCP session object (auto-provided when registered)
modelId - MCP model identifier (typically "mcp-sampling")
messages - Array of message objects

Optional Parameters

temperature - Creativity control (if supported by MCP client)
maxTokens - Maximum response length (if supported)
schema - JSON schema for structured output (generateObject only)

Security Considerations

Session Security: MCP sessions should be properly authenticated
Server Validation: Only connect to trusted MCP servers
Data Privacy: Ensure MCP clients handle data according to your privacy requirements
Error Exposure: Be careful not to expose sensitive session information in error messages

Future Enhancements

Planned improvements for MCP provider:

Native Streaming Support - True streaming for compatible MCP clients (requires MCP protocol updates)
Enhanced Session Monitoring - Automatic session validation and recovery
Performance Optimization - Caching and connection pooling
Advanced Error Recovery - Intelligent retry and fallback strategies

Note: True streaming support depends on future MCP protocol enhancements. Current implementation provides text generation without streaming capabilities.

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