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Ralph Khreish fbea48d8ec chore: fix configuration file

2025-07-12 00:54:16 +03:00

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Configuration

Taskmaster uses two primary methods for configuration:

.taskmaster/config.json File (Recommended - New Structure)

This JSON file stores most configuration settings, including AI model selections, parameters, logging levels, and project defaults.
Location: This file is created in the .taskmaster/ directory when you run the task-master models --setup interactive setup or initialize a new project with task-master init.
Migration: Existing projects with .taskmasterconfig in the root will continue to work, but should be migrated to the new structure using task-master migrate.
Management: Use the task-master models --setup command (or models MCP tool) to interactively create and manage this file. You can also set specific models directly using task-master models --set-<role>=<model_id>, adding --ollama or --openrouter flags for custom models. Manual editing is possible but not recommended unless you understand the structure.

Example Structure:

{
  "models": {
    "main": {
      "provider": "anthropic",
      "modelId": "claude-3-7-sonnet-20250219",
      "maxTokens": 64000,
      "temperature": 0.2,
      "baseURL": "https://api.anthropic.com/v1"
    },
    "research": {
      "provider": "perplexity",
      "modelId": "sonar-pro",
      "maxTokens": 8700,
      "temperature": 0.1,
      "baseURL": "https://api.perplexity.ai/v1"
    },
    "fallback": {
      "provider": "anthropic",
      "modelId": "claude-3-5-sonnet",
      "maxTokens": 64000,
      "temperature": 0.2
    }
  },
  "global": {
    "logLevel": "info",
    "debug": false,
    "defaultNumTasks": 10,
    "defaultSubtasks": 5,
    "defaultPriority": "medium",
    "defaultTag": "master",
    "projectName": "Your Project Name",
    "ollamaBaseURL": "http://localhost:11434/api",
    "azureBaseURL": "https://your-endpoint.azure.com/openai/deployments",
    "vertexProjectId": "your-gcp-project-id",
    "vertexLocation": "us-central1",
    "responseLanguage": "English"
  }
}

For MCP-specific setup and troubleshooting, see Provider-Specific Configuration.

Legacy .taskmasterconfig File (Backward Compatibility)
- For projects that haven't migrated to the new structure yet.
- Location: Project root directory.
- Migration: Use task-master migrate to move this to .taskmaster/config.json.
- Deprecation: While still supported, you'll see warnings encouraging migration to the new structure.

Environment Variables (`.env` file or MCP `env` block - For API Keys Only)

Used exclusively for sensitive API keys and specific endpoint URLs.
Location:
- For CLI usage: Create a .env file in your project root.
- For MCP/Cursor usage: Configure keys in the env section of your .cursor/mcp.json file.
Required API Keys (Depending on configured providers):
- ANTHROPIC_API_KEY: Your Anthropic API key.
- PERPLEXITY_API_KEY: Your Perplexity API key.
- OPENAI_API_KEY: Your OpenAI API key.
- GOOGLE_API_KEY: Your Google API key (also used for Vertex AI provider).
- MISTRAL_API_KEY: Your Mistral API key.
- AZURE_OPENAI_API_KEY: Your Azure OpenAI API key (also requires AZURE_OPENAI_ENDPOINT).
- OPENROUTER_API_KEY: Your OpenRouter API key.
- XAI_API_KEY: Your X-AI API key.
Optional Endpoint Overrides:
- Per-role baseURL in .taskmasterconfig: You can add a baseURL property to any model role (main, research, fallback) to override the default API endpoint for that provider. If omitted, the provider's standard endpoint is used.
- Environment Variable Overrides (<PROVIDER>_BASE_URL): For greater flexibility, especially with third-party services, you can set an environment variable like OPENAI_BASE_URL or MISTRAL_BASE_URL. This will override any baseURL set in the configuration file for that provider. This is the recommended way to connect to OpenAI-compatible APIs.
- AZURE_OPENAI_ENDPOINT: Required if using Azure OpenAI key (can also be set as baseURL for the Azure model role).
- OLLAMA_BASE_URL: Override the default Ollama API URL (Default: http://localhost:11434/api).
- VERTEX_PROJECT_ID: Your Google Cloud project ID for Vertex AI. Required when using the 'vertex' provider.
- VERTEX_LOCATION: Google Cloud region for Vertex AI (e.g., 'us-central1'). Default is 'us-central1'.
- GOOGLE_APPLICATION_CREDENTIALS: Path to service account credentials JSON file for Google Cloud auth (alternative to API key for Vertex AI).

Important: Settings like model ID selections (main, research, fallback), maxTokens, temperature, logLevel, defaultSubtasks, defaultPriority, and projectName are managed in .taskmaster/config.json (or .taskmasterconfig for unmigrated projects), not environment variables.

Tagged Task Lists Configuration (v0.17+)

Taskmaster includes a tagged task lists system for multi-context task management.

Global Tag Settings

"global": {
  "defaultTag": "master"
}

defaultTag (string): Default tag context for new operations (default: "master")

Git Integration

Task Master provides manual git integration through the --from-branch option:

Manual Tag Creation: Use task-master add-tag --from-branch to create a tag based on your current git branch name
User Control: No automatic tag switching - you control when and how tags are created
Flexible Workflow: Supports any git workflow without imposing rigid branch-tag mappings

State Management File

Taskmaster uses .taskmaster/state.json to track tagged system runtime information:

{
  "currentTag": "master",
  "lastSwitched": "2025-06-11T20:26:12.598Z",
  "migrationNoticeShown": true
}

currentTag: Currently active tag context
lastSwitched: Timestamp of last tag switch
migrationNoticeShown: Whether migration notice has been displayed

This file is automatically created during tagged system migration and should not be manually edited.

Example `.env` File (for API Keys)

# Required API keys for providers configured in .taskmaster/config.json
ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
PERPLEXITY_API_KEY=pplx-your-key-here
# OPENAI_API_KEY=sk-your-key-here
# GOOGLE_API_KEY=AIzaSy...
# AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
# etc.

# Optional Endpoint Overrides
# Use a specific provider's base URL, e.g., for an OpenAI-compatible API
# OPENAI_BASE_URL=https://api.third-party.com/v1
#
# Azure OpenAI Configuration
# AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/ or https://your-endpoint-name.cognitiveservices.azure.com/openai/deployments
# OLLAMA_BASE_URL=http://custom-ollama-host:11434/api

# Google Vertex AI Configuration (Required if using 'vertex' provider)
# VERTEX_PROJECT_ID=your-gcp-project-id

Troubleshooting

Configuration Errors

If Task Master reports errors about missing configuration or cannot find the config file, run task-master models --setup in your project root to create or repair the file.
For new projects, config will be created at .taskmaster/config.json. For legacy projects, you may want to use task-master migrate to move to the new structure.
Ensure API keys are correctly placed in your .env file (for CLI) or .cursor/mcp.json (for MCP) and are valid for the providers selected in your config file.

If `task-master init` doesn't respond:

Try running it with Node directly:

node node_modules/claude-task-master/scripts/init.js

Or clone the repository and run:

git clone https://github.com/eyaltoledano/claude-task-master.git
cd claude-task-master
node scripts/init.js

Provider-Specific Configuration

MCP (Model Context Protocol) Provider

Prerequisites:
- An active MCP session with sampling capability
- MCP client with sampling support (e.g. VS Code)
- No API keys required (uses session-based authentication)

Configuration:

{
  "models": {
    "main": {
      "provider": "mcp",
      "modelId": "mcp-sampling"
    },
    "research": {
      "provider": "mcp",
      "modelId": "mcp-sampling"
    }
  }
}

Available Model IDs:
- mcp-sampling - General text generation using MCP client sampling (supports all roles)
- claude-3-5-sonnet-20241022 - High-performance model for general tasks (supports all roles)
- claude-3-opus-20240229 - Enhanced reasoning model for complex tasks (supports all roles)
Features:
- ✅ Text Generation: Standard AI text generation via MCP sampling
- ✅ Object Generation: Full schema-driven structured output generation
- ✅ PRD Parsing: Parse Product Requirements Documents into structured tasks
- ✅ Task Creation: AI-powered task creation with validation
- ✅ Session Management: Automatic session detection and context handling
- ✅ Error Recovery: Robust error handling and fallback mechanisms
Usage Requirements:
- Must be running in an MCP context (session must be available)
- Session must provide clientCapabilities.sampling capability
Best Practices:
- Always configure a non-MCP fallback provider
- Use mcp for main/research roles when in MCP environments
- Test sampling capability before production use

Setup Commands:

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

# Set MCP provider for research role  
task-master models set-research --provider mcp --model claude-3-opus-20240229

# Verify configuration
task-master models list

Troubleshooting:
- "MCP provider requires session context" → Ensure running in MCP environment
- See the MCP Provider Guide for detailed troubleshooting

Google Vertex AI Configuration

Google Vertex AI is Google Cloud's enterprise AI platform and requires specific configuration:

Prerequisites:
- A Google Cloud account with Vertex AI API enabled
- Either a Google API key with Vertex AI permissions OR a service account with appropriate roles
- A Google Cloud project ID
Authentication Options:
- API Key: Set the GOOGLE_API_KEY environment variable
- Service Account: Set GOOGLE_APPLICATION_CREDENTIALS to point to your service account JSON file
Required Configuration:
- Set VERTEX_PROJECT_ID to your Google Cloud project ID
- Set VERTEX_LOCATION to your preferred Google Cloud region (default: us-central1)

Example Setup:

# In .env file
GOOGLE_API_KEY=AIzaSyXXXXXXXXXXXXXXXXXXXXXXXXX
VERTEX_PROJECT_ID=my-gcp-project-123
VERTEX_LOCATION=us-central1

Or using service account:

# In .env file
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
VERTEX_PROJECT_ID=my-gcp-project-123
VERTEX_LOCATION=us-central1

In .taskmaster/config.json:

"global": {
  "vertexProjectId": "my-gcp-project-123",
  "vertexLocation": "us-central1"
}

Azure OpenAI Configuration

Azure OpenAI provides enterprise-grade OpenAI models through Microsoft's Azure cloud platform and requires specific configuration:

Prerequisites:
- An Azure account with an active subscription
- Azure OpenAI service resource created in the Azure portal
- Azure OpenAI API key and endpoint URL
- Deployed models (e.g., gpt-4o, gpt-4o-mini, gpt-4.1, etc) in your Azure OpenAI resource
Authentication:
- Set the AZURE_OPENAI_API_KEY environment variable with your Azure OpenAI API key
- Configure the endpoint URL using one of the methods below

Configuration Options:

Option 1: Using Global Azure Base URL (affects all Azure models)

// In .taskmaster/config.json
{
  "models": {
    "main": {
      "provider": "azure",
      "modelId": "gpt-4o",
      "maxTokens": 16000,
      "temperature": 0.7
    },
    "fallback": {
      "provider": "azure", 
      "modelId": "gpt-4o-mini",
      "maxTokens": 10000,
      "temperature": 0.7
    }
  },
  "global": {
    "azureBaseURL": "https://your-resource-name.azure.com/openai/deployments"
  }
}

Option 2: Using Per-Model Base URLs (recommended for flexibility)

// In .taskmaster/config.json
{
  "models": {
    "main": {
      "provider": "azure",
      "modelId": "gpt-4o", 
      "maxTokens": 16000,
      "temperature": 0.7,
      "baseURL": "https://your-resource-name.azure.com/openai/deployments"
    },
    "research": {
      "provider": "perplexity",
      "modelId": "sonar-pro",
      "maxTokens": 8700,
      "temperature": 0.1
    },
    "fallback": {
      "provider": "azure",
      "modelId": "gpt-4o-mini",
      "maxTokens": 10000, 
      "temperature": 0.7,
      "baseURL": "https://your-resource-name.azure.com/openai/deployments"
    }
  }
}

Environment Variables:

# In .env file
AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here

# Optional: Override endpoint for all Azure models
AZURE_OPENAI_ENDPOINT=https://your-resource-name.azure.com/openai/deployments

Important Notes:
- Model Deployment Names: The modelId in your configuration should match the deployment name you created in Azure OpenAI Studio, not the underlying model name
- Base URL Priority: Per-model baseURL settings override the global azureBaseURL setting
- Endpoint Format: When using per-model baseURL, use the full path including /openai/deployments
Troubleshooting:

"Resource not found" errors:
- Ensure your baseURL includes the full path: https://your-resource-name.openai.azure.com/openai/deployments
- Verify that your deployment name in modelId exactly matches what's configured in Azure OpenAI Studio
- Check that your Azure OpenAI resource is in the correct region and properly deployed
Authentication errors:
- Verify your AZURE_OPENAI_API_KEY is correct and has not expired
- Ensure your Azure OpenAI resource has the necessary permissions
- Check that your subscription has not been suspended or reached quota limits
Model availability errors:
- Confirm the model is deployed in your Azure OpenAI resource
- Verify the deployment name matches your configuration exactly (case-sensitive)
- Ensure the model deployment is in a "Succeeded" state in Azure OpenAI Studio
- Ensure youre not getting rate limited by maxTokens maintain appropriate Tokens per Minute Rate Limit (TPM) in your deployment.

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