BGPT MCP + REST API

Search scientific papers from Claude, Cursor, any MCP-compatible AI tool, or plain Python.

BGPT is a remote Model Context Protocol (MCP) server and traditional JSON/HTTP API that gives AI assistants and Python apps access to a database of scientific papers built from full-text studies. Unlike typical search tools that return titles and abstracts, BGPT extracts raw experimental data — methods, results, conclusions, quality scores, sample sizes, limitations, and 25+ metadata fields per paper.

Quick Start

Use BGPT from Python, REST, or an MCP client — no API key required for the free tier (50 free results).

Option A: Python / REST API

Call the HTTP API directly from any Python script or notebook:

import requests


def search_bgpt(query, num_results=10, days_back=None, api_key=None):
    payload = {"query": query, "num_results": num_results}
    if days_back is not None:
        payload["days_back"] = days_back
    if api_key:
        payload["api_key"] = api_key

    response = requests.post(
        "https://bgpt.pro/api/mcp-search",
        json=payload,
        timeout=30,
    )
    response.raise_for_status()
    return response.json()["results"]


papers = search_bgpt("CRISPR delivery neurons", num_results=5)
print(papers[0]["title"])

Option B: Remote MCP Connection

Most modern MCP clients support direct remote connections. BGPT offers two transports:

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "bgpt": {
      "url": "https://bgpt.pro/mcp/sse"
    }
  }
}

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "bgpt": {
      "url": "https://bgpt.pro/mcp/sse"
    }
  }
}

Claude Code (CLI):

claude mcp add bgpt --transport sse https://bgpt.pro/mcp/sse

Cline / Roo Code / Windsurf — same config:

{
  "mcpServers": {
    "bgpt": {
      "url": "https://bgpt.pro/mcp/sse"
    }
  }
}

Tip: If your client supports Streamable HTTP, you can use https://bgpt.pro/mcp/stream instead.

Option C: Via npx (for clients that need a local command)

{
  "mcpServers": {
    "bgpt": {
      "command": "npx",
      "args": ["-y", "bgpt-mcp"]
    }
  }
}

Option D: Install globally

npm install -g bgpt-mcp

Then add to your MCP config:

{
  "mcpServers": {
    "bgpt": {
      "command": "bgpt-mcp"
    }
  }
}

Any MCP Client

Connect to either endpoint:

SSE:              https://bgpt.pro/mcp/sse
Streamable HTTP:  https://bgpt.pro/mcp/stream

That's it. No Docker, no build step.

What You Get

BGPT exposes the same scientific-paper search through an MCP tool and a REST endpoint.

REST endpoint

POST https://bgpt.pro/api/mcp-search

MCP tool

search_papers

What comes back

Each paper result includes 25+ fields, extracted from the full text:

• Title & DOI — standard identifiers
• Methods — experimental design, techniques used
• Results — raw findings, measurements, statistical outcomes
• Conclusions — what the authors determined
• Quality scores — methodological rigor assessment
• Sample sizes — participant/specimen counts
• Limitations — acknowledged weaknesses
• And more — funding, conflicts of interest, study type, etc.

Example

Ask your AI assistant:

"Search for recent papers on CAR-T cell therapy response rates"

BGPT returns structured experimental data your AI can reason over — not just a list of titles.

Pricing

How It Works

Your AI Assistant (Claude, Cursor, etc.)
        │
        │  MCP Protocol (SSE or Streamable HTTP)
        ▼
   BGPT MCP / REST API
   https://bgpt.pro/mcp/sse
   https://bgpt.pro/mcp/stream
   https://bgpt.pro/api/mcp-search
        │
        │  search_papers(query, ...)
        ▼
   BGPT Paper Database
   (full-text extracted data)
        │
        ▼
   Structured Results
   (methods, results, quality scores, 25+ fields)

BGPT is a hosted remote service — your MCP client connects via SSE or Streamable HTTP, or your app calls the REST endpoint directly. No Docker, scraping, or local index required.

Use Cases

• Literature reviews — Ask your AI to survey a topic with real experimental data
• Python notebooks — Pull recent paper evidence into analysis workflows with one HTTP call
• Evidence synthesis — Ground AI responses in actual study findings
• Research assistance — Find papers by methodology, outcome, or recency
• Fact-checking — Verify claims against published experimental results
• Grant writing — Quickly gather supporting evidence for proposals

Configuration Reference

Server Details

Full MCP Client Config

{
  "mcpServers": {
    "bgpt": {
      "url": "https://bgpt.pro/mcp/sse"
    }
  }
}

Documentation

Full documentation, FAQ, and setup guides: bgpt.pro/mcp

OpenAPI spec for the REST endpoint: openapi.yaml

Additional REST discovery assets:

• apis.json — machine-readable API discovery metadata
• llms.txt — AI-crawler and agent-friendly product context
• AGENTS.md — integration guidance for AI agents
• USE_CASES.md — RAG, systematic review, notebook, and dashboard use cases
• PROMPT_GALLERY.md — ready-to-use prompts for scientific RAG, agents, integrity checks, and visual demos
• CITATION.cff and codemeta.json — research-software metadata
• examples/bgpt_rest_python.py — Python requests example
• examples/bgpt_rest_javascript.mjs — JavaScript fetch example
• examples/bgpt_rest_curl.sh — cURL example
• examples/bgpt_plotly_evidence_dashboard.py — Plotly evidence dashboard demo
• examples/postman_collection.json — importable Postman collection

Support

• Email: contact@bgpt.pro
• Issues: GitHub Issues
• API Key / Billing: bgpt.pro/mcp

Contributing

See CONTRIBUTING.md for guidelines on reporting bugs, requesting features, and contributing.

License

This repository (documentation, examples, and configuration files) is licensed under the MIT License.

The BGPT MCP API service itself is operated by BGPT and subject to its own terms of service.