"Deep Search MCP (Master Control Program)" matching MCP servers:

An orchestration system that breaks down user prompts into actionable tasks and delegates them to specialized agents (Roo Modes) using LangChain.

AI 規格大師 — MCP server bridging specs (Linear / JIRA / GitHub Issues / Notion / Markdown / Figma) to tests, with bidirectional traceability and a spec-quality coach. Sibling to mk-qa-master.

MCP server bridging product ideas to prioritized roadmaps + spec drafts. Hands directly off to mk-spec-master. (AI 規劃大師)

A local knowledge graph MCP server that provides AI agents with permanent, structured memory about codebases, enabling semantic search, blast radius analysis, and convention enforcement.

Enables deep research tasks using a multi-agent architecture that integrates any LLM and MCP tools. Available via MCP stdio, streamable HTTP, and SSE transports.

Free, open-source search engine MCP server with 7 sources, 29 tools, and semantic search via ChromaDB at zero cost.

Enables multi-agent code review and iterative improvement loop using different LLM backends, with deterministic scoring and audit trail.

Enables AI assistants to perform deep web research and generate comprehensive reports using a multi-agent divide and conquer approach.

Centralized MCP server that intelligently routes user queries to appropriate MCP tools, aggregates responses from multiple servers, and supports both rule-based and AI-powered routing.

A multi-agent research system that decomposes complex queries into targeted sub-questions, searches the web in parallel, scores source credibility, and synthesizes findings into structured markdown reports.

An open-source deep research MCP server that provides multi-source web search and synthesis with citations, enabling agents to perform citation-backed research using Qwen3-30B-A3B-Thinking and other models.

An MCP server that exposes Gemini's Deep Research Agent for comprehensive web research.

A Python-based agent that integrates research providers (OpenAI, Gemini, DR-Tulu, Open Deep Research) with Claude Code via the Model Context Protocol for automated deep research.

mcp-test-runner is an MCP server that lets your AI client (Claude / Cursor / Codex / Gemini) drive your entire QA loop end-to-end: * Run tests across pytest / Jest / Cypress / Go / Maestro — single MCP surface, one env var to switch * Analyze a URL (Web DOM probe) or a live mobile screen (Maestro hierarchy) to extract testable modules + candidate cases * Generate runna

Enables users to create and refine a research outline, queue report generation in the background, and retrieve progress or completed Markdown reports, with automatic checkpointing so unfinished work can resume after a restart.

Turns Canvas LMS into a scriptable pipeline and exposes it to AI assistants via MCP for querying courses, grades, assignments, and syncing files using natural language.

An MCP server that provides a structured, persistent scratchpad for hard reasoning, with staged reasoning and self-critique, built for local models.

Enables AI tools to access local crypto market data, run analyses, perform 66-item deep research, search RAG context, and retrieve generated reports and prompts via MCP.

Portable governance control-promotion CLI and MCP server.

Turns Claude Code into a recursive analysis engine that builds explorable decision trees from IF/THEN chains, enabling thorough problem exploration and structured synthesis of action plans.