🔗 MCP Integration Now Live 🥳

GPT Researcher now supports Model Context Protocol (MCP) - connect to specialized data sources alongside web search for comprehensive research.

Key Features

• Two-stage intelligent approach: Auto-selects relevant tools and generates contextual research
• Hybrid strategies: Combine web search with MCP servers (RETRIEVER=tavily,mcp)
• Multi-server support: GitHub, financial APIs, academic databases, custom tools
• Zero-config optimization: Works out-of-the-box with sensible defaults

Quick Start

from gpt_researcher import GPTResearcher
import os

# Enable hybrid research
os.environ["RETRIEVER"] = "tavily,mcp"

researcher = GPTResearcher(
    query="What are the latest React patterns?",
    mcp_configs=[{
        "name": "github",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-github"],
        "env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")}
    }]
)

context = await researcher.conduct_research()
report = await researcher.write_report()

Use Cases

• Code research: GitHub repos, documentation, technical analysis
• Financial analysis: Market data, stock trends, business intelligence
• Academic research: ArXiv papers, research databases
• Enterprise integration: Internal systems, compliance workflows

Full MCP Documentation | Examples

What's Changed

• MCP client-server Integration for GPTR by @assafelovic in #1424
• Fix #1418: OSError: [Errno 36] File name too long with long Chinese prompt by @qylf0000 in #1428

New Contributors

• @qylf0000 made their first contribution in #1428

Full Changelog: v3.2.9...v3.3.0