Gpt Researcher Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Gpt Researcher Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
#### 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"
}
}<details>
To prepare the package for distribution:
uv syncuv buildThis will create source and wheel distributions in the dist/ directory.
uv publishNote: You'll need to set PyPI credentials via environment variables or command flags:
--token or UV_PUBLISH_TOKEN--username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORDSince 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-mcpUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging. </details>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.