Taskmaster
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| XAI_API_KEY | No | API key for xAI (for research or main model) | |
| GOOGLE_API_KEY | No | API key for Google Gemini models | |
| OLLAMA_API_KEY | No | API key for Ollama models | |
| OPENAI_API_KEY | No | API key for OpenAI models | |
| MISTRAL_API_KEY | No | API key for Mistral models | |
| ANTHROPIC_API_KEY | No | API key for Anthropic Claude models | |
| OPENROUTER_API_KEY | No | API key for OpenRouter (for research or main model) | |
| PERPLEXITY_API_KEY | No | API key for Perplexity (for research model) | |
| AZURE_OPENAI_API_KEY | No | API key for Azure OpenAI models |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| initialize_projectA | Initializes a new Task Master project structure by calling the core initialization logic. Creates necessary folders and configuration files for Task Master in the current directory. |
| modelsB | Get information about available AI models or set model configurations. Run without arguments to get the current model configuration and API key status for the selected model providers. |
| rulesB | Add or remove rule profiles from the project. |
| parse_prdA | Parse a Product Requirements Document (PRD) text file to automatically generate initial tasks. Reinitializing the project is not necessary to run this tool. It is recommended to run parse-prd after initializing the project and creating/importing a prd.txt file in the project root's .taskmaster/docs directory. |
| analyze_project_complexityB | Analyze task complexity and generate expansion recommendations. |
| expand_taskC | Expand a task into subtasks for detailed implementation |
| expand_allC | Expand all pending tasks into subtasks based on complexity or defaults |
| scope_up_taskC | Increase the complexity of one or more tasks using AI |
| scope_down_taskC | Decrease the complexity of one or more tasks using AI |
| get_tasksC | Get all tasks from Task Master, optionally filtering by status and including subtasks. |
| get_taskC | Get detailed information about a specific task |
| next_taskB | Find the next task to work on based on dependencies and status |
| complexity_reportC | Display the complexity analysis report in a readable format |
| set_task_statusC | Set the status of one or more tasks or subtasks. |
| generateC | Generates individual task files in tasks/ directory based on tasks.json |
| add_taskC | Add a new task using AI |
| add_subtaskC | Add a subtask to an existing task |
| updateA | Update multiple upcoming tasks (with ID >= 'from' ID) based on new context or changes provided in the prompt. Use 'update_task' instead for a single specific task or 'update_subtask' for subtasks. |
| update_taskC | Updates a single task by ID with new information or context provided in the prompt. |
| update_subtaskA | Appends timestamped information to a specific subtask without replacing existing content. If you just want to update the subtask status, use set_task_status instead. |
| remove_taskB | Remove a task or subtask permanently from the tasks list |
| remove_subtaskC | Remove a subtask from its parent task |
| clear_subtasksC | Clear subtasks from specified tasks |
| move_taskC | Move a task or subtask to a new position |
| add_dependencyC | Add a dependency relationship between two tasks |
| remove_dependencyC | Remove a dependency from a task |
| validate_dependenciesA | Check tasks for dependency issues (like circular references or links to non-existent tasks) without making changes. |
| fix_dependenciesC | Fix invalid dependencies in tasks automatically |
| response-languageB | Get or set the response language for the project |
| list_tagsB | List all available tags with task counts and metadata |
| add_tagC | Create a new tag for organizing tasks in different contexts |
| delete_tagB | Delete an existing tag and all its tasks |
| use_tagC | Switch to a different tag context for task operations |
| rename_tagC | Rename an existing tag |
| copy_tagC | Copy an existing tag to create a new tag with all tasks and metadata |
| researchC | Perform AI-powered research queries with project context |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 36 tools
The tool set has clear distinctions for core task operations like add_task, get_task, and update_task, but there is significant overlap in complexity management tools (expand_task, expand_all, scope_up_task, scope_down_task, analyze_project_complexity, complexity_report) and in update operations (update, update_task, update_subtask), which could confuse agents about which to use. Descriptions help clarify some differences, but the boundaries are fuzzy.
Most tools follow a consistent verb_noun or verb_adjective_noun pattern (e.g., add_task, get_tasks, set_task_status), with clear and readable names. There are minor deviations like 'models' and 'response-language' that use different conventions, but overall the naming is predictable and aids usability.
With 36 tools, the count is excessive for a task management server, leading to a heavy and potentially overwhelming interface. While the domain is broad, many tools (e.g., expand_all, scope_up_task, complexity_report) could be consolidated or made optional, making the set feel bloated and difficult to navigate efficiently.
The tool surface provides comprehensive coverage for task management, including full CRUD operations (add_task, get_task, update_task, remove_task), dependency handling, tagging, subtask management, project initialization, and advanced features like AI integration and complexity analysis. There are no obvious gaps; agents can handle end-to-end workflows without dead ends.