| name | gpt-researcher |
| description | Autonomous multi-source research agent with MCP server. Planner/executor architecture, 5 MCP tools. Requires 3 API keys (Tavily + Anthropic + embeddings). Research-flavor.
|
| auto-activate | false |
gpt-researcher
Autonomous multi-source research agent with a planner/executor architecture. Gathers information from multiple sources in parallel.
Upstream: https://github.com/assafelovic/gpt-researcher
MCP Server: https://github.com/assafelovic/gptr-mcp
MCP Server Setup
Add to your project's .mcp.json:
{
"gpt-researcher": {
"type": "stdio",
"command": "uvx",
"args": ["gptr-mcp"],
"env": {
"TAVILY_API_KEY": "<your-key>",
"ANTHROPIC_API_KEY": "<your-key>",
"FAST_LLM": "anthropic:<your-fast-model>",
"SMART_LLM": "anthropic:<your-smart-model>"
}
}
}
Replace <your-fast-model> and <your-smart-model> with your preferred Claude model IDs before use.
MCP Tools
| Tool | Purpose |
|---|
deep_research | Full multi-source research on a topic |
quick_search | Fast single-source lookup |
write_report | Generate structured report from research |
get_research_sources | List sources used in research |
get_research_context | Retrieve accumulated research context |
API Keys Required
- TAVILY_API_KEY — search engine (required)
- ANTHROPIC_API_KEY — LLM provider (required)
- Embedding provider key — for semantic search (required)
How It Complements Loam
/researcher — quick single-pass web research- GPT-Researcher — autonomous multi-source deep research with structured reports
Requirements
- Python 3.11+
- Three API keys minimum
- MCP server is from separate repo (
gptr-mcp), early-stage
Notes
- Apache-2.0 license
- Originally OpenAI-focused, but has native Anthropic provider support
- MCP server is early-stage (separate repo) — expect rough edges
- Per-invocation cost varies by research depth
