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gpt-researcher-mcp

gpt-researcher-mcp

by markchiang
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

gpt-researcher-mcp MCP server

A MCP server for gpt-researcher

This is just a MCP wrapper for gpt-researcher.

This project, gpt-researcher-mcp, is a wrapper extension built on top of the open-source project gpt-researcher. The original gpt-researcher was developed by Assaf Elovic, and I am not the original author.

gpt-researcher is an automated research assistant that performs web searches, summarizes information, and generates structured reports based on a given query. This wrapper (gpt-researcher-mcp) is designed to make the original tool more suitable for integration into MCP or similar workflows.

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

  "gpt-researcher-mcp": {
    "command": "npx",
    "args": ["-y", "gpt-researcher-mcp"],
    "env": {
      "llm_provider": "google_genai",
      "GOOGLE_API_KEY": "your key",
      "FAST_LLM": "google_genai:gemini-1.5-flash",
      "SMART_LLM": "google_genai:gemini-1.5-pro",
      "STRATEGIC_LLM":"google_genai:gemini-1.5-pro",
      "EMBEDDING": "google_genai:models/text-embedding-004",
      "TAVILY_API_KEY": "your key"
    }
  }

Related MCP server: Gemini Research MCP Server

Development

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:

uv sync

  1. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN

  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv run gpt-researcher-mcp

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Available Tools

2 tools
add-noteC

Add a new note

Inputs: name (required), content (required).

get_reportC

Get a research report based on a query and report type

Inputs: query (required), report_type.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.1/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools, add-note and get_report, have completely distinct purposes with no overlap. An agent can easily tell them apart based on their names and descriptions.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern using hyphens (add-note, get-report). This is a clear and predictable naming convention.

    Tool Count3/5

    With only two tools, the server feels thin and borderline under-scoped for a research-focused MCP server. Two tools fall into the lower end of acceptable range but do not feel entirely insufficient.

    Completeness2/5

    The server lacks core operations for a research workflow, such as initiating research, listing past reports, or managing notes beyond adding. Significant gaps exist that would likely cause agent failures in practical use.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

    Resources

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