deep-research-mcp
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- FlicenseNot gradedqualityBmaintenanceTurns deep-research questions into reusable, source-grounded Workspaces with review checkpoints and background tasks, enabling structured research and reporting.-
- AlicenseNot gradedqualityNot gradedmaintenanceConnects AI assistants to the Maestro research framework to orchestrate multi-agent research missions, including planning, research, and writing phases. It enables users to launch research tasks, track real-time progress, and retrieve comprehensive structured reports and notes.-
- AlicenseNot gradedqualityFmaintenanceEnables writers and researchers to manage large Markdown documents with AI-powered tools, including version history, semantic search, and context management.MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI-powered research by breaking a topic into subtopics, gathering information via agents, and compiling a report. Integrates with LangGraph and RAG for orchestration and contextual retrieval.1-
- AlicenseNot gradedqualityCmaintenanceEnables users to run autonomous multi-stage deep research directly from MCP-compatible clients, producing cited, publication-quality technical reports from web searches and unstructured data.1MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to save, search, and manage markdown-based research articles through a complete CRUD interface. Supports creating, reading, updating, and deleting articles with frontmatter metadata in a self-hosted file-based system.2MIT
TDQS
Scored across 4 tools
Each tool maps to a distinct phase of the report lifecycle: plan creation, outline update, generation, and progress/result retrieval. There is no meaningful overlap between read and write operations.
Tool names consistently use snake_case verb_noun structure (get_report, create_report_plan, update_report_outline, generate_report). Minor deviation: generate_report acts on the same report object as get_report, but the verb clearly differentiates the action.
Four tools cover a focused asynchronous research workflow without redundancy. This is a well-scoped set for the server's purpose.
Core lifecycle is covered: create plan, update outline, generate report, and read progress/results. Missing operations like cancel or delete are minor gaps that agents can work around.