While LLM apps can access web search tools with MCP, **GPT Researcher MCP delivers deep research results.** Standard search tools return raw results
Gptr MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. While LLM apps can access web search tools with MCP, GPT Researcher MCP delivers deep research results. Standard search tools return raw results requiring.
Once Gptr MCP is connected, these are the calls the assistant has available:
deep_research — Performs deep web research on a topic, finding the most reliable and relevant informationquick_search — Performs a fast web search optimized for speed over quality, returning search results with snippets. Supports any GPTR supported web retriever suchwrite_report — Generate a report based on research resultsget_research_sources — Get the sources used in the researchget_research_context — Get the full context of the researchResources — The Resources tool exposed by this serverPrompts — The Prompts tool exposed by this serverSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
You will need 5 environment variables: OPENAI_API_KEY, TAVILY_API_KEY, MCP_TRANSPORT, YOUR_SESSION_ID, YOUR_ID. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Before running the MCP server, make sure you have: 1. Python 3.11 or higher installed - Important: GPT Researcher >=0.12.16 requires Python 3.11+ 2. API keys for the services you plan to use: - OpenAI API key - Tavily API key You can also connect any other web search engines or MCP using GPTR supported retrievers. Check out the
Among the search and retrieval options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Gptr MCP's toolset — deep_research, quick_search, write_report and 4 more — is a fair guide to whether it matches your workflow. It is maintained by assafelovic; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| deep_research | Performs deep web research on a topic, finding the most reliable and relevant information |
| quick_search | Performs a fast web search optimized for speed over quality, returning search results with snippets. Supports any GPTR supported web retriever such as Tavily, Bing, Google, etc... Learn more [here](https://docs.gptr.dev/ |
| write_report | Generate a report based on research results |
| get_research_sources | Get the sources used in the research |
| get_research_context | Get the full context of the research |
| Resources | The Resources tool exposed by this server. |
| Prompts | The Prompts tool exposed by this server. |
{
"mcpServers": {
"gptr-mcp": {
"command": "python",
"args": ["/absolute/path/to/your/server.py"],
"env": {
"OPENAI_API_KEY": "your-actual-openai-key-here",
"TAVILY_API_KEY": "your-actual-tavily-key-here"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
Before running the MCP server, make sure you have: 1. Python 3.11 or higher installed - Important: GPT Researcher >=0.12.16 requires Python 3.11+ 2. API keys for the services you plan to use: - OpenAI API key - Tavily API key You can also connect any other web search engines or MCP using GPTR supported retrievers. Check out the
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| TAVILY_API_KEY | Credential the server authenticates with. | Yes |
| MCP_TRANSPORT | Configuration value read at startup. | Optional |
| YOUR_SESSION_ID | Configuration value read at startup. | Optional |
| YOUR_ID | Configuration value read at startup. | Optional |
The simplest web tool that matters — fetch any URL and get model-ready markdown back.
Industrial-strength web extraction — render, scrape, crawl and search entire sites into clean markdown.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Put 6,000+ pre-built scrapers at your assistant's fingertips through one MCP endpoint.