Task Researcher MCP Server

Researcher for AI Coding that analyzes task complexity and runs deep research (STORM) to decompose complex tasks into subtasks, as an MCP Server or

Local serverstdioPython

What is the Task Researcher MCP Server?

Researcher for AI Coding that analyzes task complexity and runs deep research (STORM) to decompose complex tasks into subtasks, as an MCP Server or CLI.

The short version

A Python task management system designed for AI-driven development, featuring integrated, in-depth research capabilities using the knowledge-storm library. Break down complex projects, generate tasks, and leverage automated research to inform implementation details.

What it needs from you

  • Python 3.10+ * An API key for at least one supported LLM provider (e.g., Anthropic, Google Gemini, OpenAI) set in a .env file (used for task generation, complexity analysis, etc.). * knowledge-storm library (pip install knowledge-storm). * API key for a search engine supported by knowledge-storm (e.g., Bing Search, You.com, Tavily) set in .env (Required for --research in expand

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

Recorded tools

Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.

ToolWhat it does
parse_inputsGenerates initial tasks from configured spec files.
update_tasksUpdates tasks from a given ID based on a prompt.
generate_task_filesCreates individual phase_XX_task_YYY.txt files.
expand_taskExpands a single task into subtasks (supports research=True for STORM).
expand_all_tasksExpands all eligible pending tasks (supports research=True).
analyze_complexityAnalyzes task complexity and saves a report.
validate_dependenciesChecks dependencies for issues.
fix_dependenciesAttempts to automatically fix dependency issues.
research_topicRuns STORM to generate a research report on a topic.

How to install the Task Researcher MCP Server

{
  "mcpServers": {
    "task-researcher": {
      "command": "uvx",
      "args": ["knowledge-storm"],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "BING_SEARCH_API_KEY": "your-value",
        "TAVILY_API_KEY": "your-value",
        "GOOGLE_API_KEY": "your-value",
        "YDC_API_KEY": "your-value",
        "TASKS_FILE_PATH": "your-value"
      }
    }
  }
}

Add to claude_desktop_config.json, then restart Claude Desktop.

Configuration

  • Python 3.10+ * An API key for at least one supported LLM provider (e.g., Anthropic, Google Gemini, OpenAI) set in a .env file (used for task generation, complexity analysis, etc.). * knowledge-storm library (pip install knowledge-storm). * API key for a search engine supported by knowledge-storm (e.g., Bing Search, You.com, Tavily) set in .env (Required for --research in expand
VariableDescriptionRequired
ANTHROPIC_API_KEYCredential the server authenticates with.Yes
BING_SEARCH_API_KEYCredential the server authenticates with.Yes
TAVILY_API_KEYCredential the server authenticates with.Yes
GOOGLE_API_KEYCredential the server authenticates with.Yes
YDC_API_KEYCredential the server authenticates with.Yes
TASKS_FILE_PATHFilesystem location the server is allowed to use.Optional

Example prompts to try

  • “Use Task Researcher to parse inputs.”
  • “Use Task Researcher to update tasks.”
  • “Use Task Researcher to generate task files.”

Frequently asked questions

`expand --research` triggers the full STORM-based workflow, performing live web searches to inform subtasks. `analyze-complexity --research-hint` modifies the prompt to ask the primary LLM to leverage its internal knowledge more deeply without any live search.