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  1. Home
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  3. MCP Server Deep Research
MCP Server Deep Research logo
Health: ActiveRecent health check succeeded.Last checked 9/11/2026, 1:31:15 PM

MCP Server Deep Research

User RatingsBe the first to rate and review this MCP server!
View Repository215 GitHub StarsTotal stargazers on GitHub for the source repository (215 stars).Visit Website
researchweb-searchreportsclaude

Turns research questions into structured, cited reports through a Claude-oriented MCP workflow.

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Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "reading-plus-ai-mcp-server-deep-research": {
      "command": "uvx",
      "args": [
        "--directory",
        "/Users/username/repos/mcp-server-application/mcp-server-deep-research",
        "run",
        "mcp-server-deep-research"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🔎 More in Search & Data Extraction

Overview

The reading-plus-ai/mcp-server-deep-research MCP server turns a research question into an expanded investigation plan, targeted web searches, and a cited report. It elaborates the question, generates subquestions, uses Claude’s built-in web search, analyzes sources, and returns a structured artifact. Reach for it when a topic needs broad coverage, multiple perspectives, and evidence-based reporting rather than a single lookup. The documented workflow is designed for Claude Desktop and exposes a deep-research prompt template.

Use cases

•Research a complex topic across multiple subquestions
•Generate cited reports from web sources
•Compare perspectives on an open-ended question
•Turn an initial question into a structured research plan

Key features

•Question elaboration and scope definition
•Automatic subquestion generation
•Claude web search for research subtopics
•Source quality and relevance analysis
•Cited report artifacts

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by MCP Server Deep Research.

Extracted Tool Capabilities
Question elaboration and scope definition
Automatic subquestion generation
Claude web search for research subtopics
Source quality and relevance analysis
Cited report artifacts

How MCP Server Deep Research works

What reading-plus-ai/mcp-server-deep-research MCP server does

The reading-plus-ai/mcp-server-deep-research MCP server provides a guided workflow for researching complex questions. Instead of treating the initial prompt as a single search query, it first clarifies the subject, identifies important terms, and establishes the intended scope. It then breaks the topic into focused subquestions so different aspects can be investigated separately.

The workflow is intended to produce a readable research report rather than a list of search results. Reports are structured as artifacts, include citations for the sources used, and present conclusions with supporting evidence. The documented process also aims to include relevant and authoritative sources and to represent different perspectives where applicable.

How it works

The main entry point described in the repository is the deep-research prompt. A user supplies a research question through the MCP workflow, and the prompt guides the subsequent stages:

  • clarify the question and define its boundaries;
  • generate subquestions for systematic coverage;
  • search the web for each subquestion using Claude’s built-in web search;
  • assess source quality and relevance;
  • combine findings from multiple sources; and
  • format the results into a cited report artifact.

The reading-plus-ai/mcp-server-deep-research MCP server therefore fits tasks where the research plan should be generated from the question itself. The README does not describe a separate search API credential, an external search provider, or a set of individually named research tools.

Setup and configuration

The README documents Claude Desktop as the client used to try the project. On macOS, the stated setup command is python setup.py. After setup, the user selects the deep-research prompt template and provides a research question.

For a published installation, the Claude Desktop configuration uses uvx with the package name mcp-server-deep-research:

config.json
{
  "mcpServers": {
    "mcp-server-deep-research": {
      "command": "uvx",
      "args": ["mcp-server-deep-research"]
    }
  }
}

Claude Desktop stores its configuration at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS and %APPDATA%/Claude/claude_desktop_config.json on Windows. For unpublished development copies, the README shows a uv configuration that points to a local project directory; that example contains a machine-specific path and is not a directly runnable install command.

Tools and capabilities

The reading-plus-ai/mcp-server-deep-research MCP server documents one prompt component: deep-research. Its supported workflow capabilities include question elaboration, subquestion generation, web search through Claude, source analysis, synthesis, citation, and report generation. The report output is described as a formatted artifact with a balanced, evidence-based presentation.

The project can also be developed and packaged with uv. The documented development commands synchronize dependencies with uv sync, build distributions with uv build, and publish them with uv publish.

Limitations and notes

The repository material describes a Claude-centered workflow and specifically instructs users to use Claude Desktop. It does not establish compatibility with other MCP clients. Web searching is described as using Claude’s built-in capability, so the README does not provide configuration for an independent search service or a search API key.

Only the deep-research prompt is listed in the components section. The available material does not specify adjustable research-depth settings, database storage, scheduled jobs, custom source connectors, or additional standalone tools. The setup instructions also differ between a local unpublished checkout and the published uvx package, so the appropriate configuration depends on whether the project is being developed locally or installed as a published server.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
215
Stargazers on the source repository.
Last commit
1y ago
Most recent push to the default branch.
Directory activity
4 views
Config copies, upvotes, and views on AllMCPs.

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Frequently Asked Questions about MCP Server Deep Research

For the published package, configure Claude Desktop to run `uvx mcp-server-deep-research`. The README also documents `python setup.py` for setup on macOS.

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Technical Specs & Signals

Category🔎Search & Data Extraction
PricingFree
More technical detailsExpand ▾
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseMIT
ClientsClaude Desktop
Last updatedSep 3, 2026
1/5 checks healthy over the last 32d
Views4
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars215
GitHub Star CountTotal stargazers on GitHub representing community popularity (215 stars).
Last commit1y ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Mar 25, 2025
51Quality signal: Good · 51/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools23/30
Adoption & activity4/15
Community engagement1/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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Critical 0High 0Medium 0Low 0

Scanned 17d ago via OSV.dev · mcp-server-deep-research (PyPI)

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