Bgpt Mcp

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.

[](https://modelcontextprotocol.io/) [](https://www.npmjs.com/package/bgpt-mcp) [](LICENSE) [](https://glama.ai/mcp/servers/connerlambden/bgpt-mcp)

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Evidence Demo

If you want to see why BGPT is different from ordinary paper search, start here:

• [EVIDENCE_DEMO.md](EVIDENCE_DEMO.md) — a claim-interrogation demo for GLP-1 alcohol craving
• [examples/bgpt_plotly_evidence_dashboard.py](examples/bgptplotlyevidence_dashboard.py) — generate a Plotly HTML dashboard with study methods, samples, limitations, conflicts, data availability, blind spots, and falsifiability prompts
• [PROMPT_GALLERY.md](PROMPT_GALLERY.md) — prompts for scientific RAG, literature review agents, and evidence dashboards

The core idea: BGPT helps an AI agent ask what would weaken this scientific claim? before it summarizes the literature.

---

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:

| Transport | Endpoint | |-----------|----------| | SSE | https://bgpt.pro/mcp/sse | | Streamable HTTP | https://bgpt.pro/mcp/stream |

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.

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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

| JSON field | Type | Required | Description | |------------|------|----------|-------------| | query | string | Yes | Search terms (e.g. "CRISPR gene editing efficiency") | | num_results | integer | No | Number of results to return (1-100, default 10) | | days_back | integer | No | Only return papers published within the last N days | | api_key | string | No | Your Stripe subscription ID for paid access |

MCP tool

search_papers

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | query | string | Yes | Search terms (e.g. "CRISPR gene editing efficiency") | | num_results | integer | No | Number of results to return (1-100, default 10) | | days_back | integer | No | Only return papers published within the last N days | | api_key | string | No | Your Stripe subscription ID for paid access |

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

| Tier | Cost | Details | |------|------|---------| | Free | $0 | 50 free results, no API key needed | | Pay-as-you-go | $0.02/result | Billed per result returned. Get an API key at bgpt.pro/mcp |

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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.

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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

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Configuration Reference

Server Details

| Field | Value | |-------|-------| | Protocol | MCP (Model Context Protocol) | | Transport | SSE (Server-Sent Events) or Streamable HTTP | | SSE Endpoint | https://bgpt.pro/mcp/sse | | Streamable HTTP Endpoint | https://bgpt.pro/mcp/stream | | REST Endpoint | https://bgpt.pro/api/mcp-search | | Authentication | None required (free tier) / Stripe API key (paid) |

Full MCP Client Config

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

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Related MCP

From the same author — news/markets bias scoring on one side, structured scientific evidence on the other:

• Helium MCP — 37-dimensional news bias scoring, market data, ML options pricing (demo)
• helium-mcp-cookbook — runnable Python recipes for Helium's REST surface

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Listed On

BGPT is indexed on several API and MCP directories (helps discovery; links are dofollow where noted):

• bio.tools — life-science software registry (biotools:bgpt)
• CLIRank — API quality score
• Glama MCP — MCP server directory
• Postman API Network — runnable collection
• Smithery — MCP registry
• cursor.directory — Cursor MCP listing

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Documentation

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

OpenAPI spec for the REST endpoint: [openapi.yaml](openapi.yaml)

Additional REST discovery assets:

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

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Support

• Email: [contact@bgpt.pro](mailto:contact@bgpt.pro)
• Issues: [GitHub Issues](../../issues)
• API Key / Billing: bgpt.pro/mcp

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Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on reporting bugs, requesting features, and contributing.

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License

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

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

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: connerlambden
• Source: connerlambden/bgpt-mcp
• License: MIT
• Homepage: https://bgpt.pro/mcp/

Install and usage instructions live in the source repository linked above.