Deep Research MCP Server — Web Search & Research for AI Agents

Give your AI agent real research skills in one tool call. Deep Research MCP searches the web, reads the top pages, and returns a clean, citation-ready Markdown dossier your LLM can answer from — built for Claude, ChatGPT, Cursor, and any MCP client, with no external API key to manage.

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What is Deep Research MCP?

It's a Model Context Protocol (MCP) server that gives an AI agent three web-research tools. Unlike search wrappers that force you to bring your own OpenAI / Perplexity / Tavily key, this runs search and page-reading on the Apify platform — your agent is the LLM, so it does the synthesis itself. No keys, no glue code, no per-failure billing surprises.

What can it do?

Tool	What it does
web_search	Search the web → a ranked list of {title, url, snippet}. Discover sources before reading them.
read_url	Fetch any URL → its main content as clean Markdown (nav, ads, and boilerplate stripped). Turn a page into LLM-ready context.
deep_research	One call: search → read the top N sources in parallel → return a citation-ready dossier. Your agent writes the cited synthesis.

How do I connect it to Claude, Cursor, or ChatGPT?

1. Click Start to run this Actor in Standby mode.
2. Point your MCP client at the server's /mcp endpoint (Streamable HTTP) or /sse (legacy).

{
  "mcpServers": {
    "deep-research": {
      "url": "https://deep-research-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN"
    }
  }
}

Then just ask: "Use deep_research to compare the top open-source vector databases in 2026 and summarize with citations." The agent calls the tool, gets a dossier of read sources, and writes a cited answer — no extra API keys, no scraping code.

What does the output look like?

{
  "query": "best open-source vector databases 2026",
  "sourcesFound": 8,
  "sourcesRead": 4,
  "sources": [
    { "index": 1, "title": "Best Open Source Vector Databases 2026", "url": "https://redis.io/blog/...", "content": "# ...clean markdown..." }
  ],
  "dossier": "# Research dossier: best open-source vector databases 2026\n\n## [1] ...cited, LLM-ready..."
}

How much does it cost?

Pay-per-event — you pay only for what your agent actually does, no subscription:

Event	Price
Actor start	$0.01
web_search	$0.005 / search
read_url	$0.004 / page
deep_research	$0.02 / report

A flat, predictable price per outcome — not opaque credits that expire, and no charge-on-failure.

Why this over Tavily, Firecrawl, or RAG Web Browser?

• No API key needed — search + reading run on-platform; your agent supplies the intelligence.
• Agentic, not just fetch — deep_research orchestrates multi-source reading and hands back a citation-ready dossier, not a single page.
• Flat per-call pricing — no credit systems, no billing on failed runs.
• MCP-native — drops straight into Claude Desktop, Cursor, Windsurf, and OpenAI/Anthropic agents.

FAQ

Do I need an OpenAI or Perplexity API key? No. The calling agent does the synthesis; this server only does search + reading.

Which MCP clients work? Claude Desktop, Cursor, Windsurf, Cline, and any Streamable-HTTP or SSE MCP client.

Can I run it without MCP? Yes — pass a query in the input for a one-off run; the dossier is saved to the dataset. Great for scheduled research.

Is the data fresh? It searches live web results. Pages published minutes ago may not be indexed yet.

Other Actors by this author

Building agent tools and lead-gen data? See my other Actors — contact & email finder, Google Maps, Reddit, Indeed, and more.

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Built and maintained by Renzo Madueño. Found a bug or want another tool? Open an issue or leave a review and tell me — I read every message.