bgpt-paper-search

BGPT Paper Search

Overview

BGPT is a remote MCP server that searches a curated database of scientific papers built from raw experimental data extracted from full-text studies. Unlike traditional literature databases that return titles and abstracts, BGPT returns structured data from the actual paper content — methods, quantitative results, sample sizes, quality assessments, and 25+ metadata fields per paper.

When to Use This Skill

Use this skill when:

• Searching for scientific papers with specific experimental details
• Conducting systematic or scoping literature reviews
• Finding quantitative results, sample sizes, or effect sizes across studies
• Comparing methodologies used in different studies
• Looking for papers with quality scores or evidence grading
• Needing structured data from full-text papers (not just abstracts)
• Building evidence tables for meta-analyses or clinical guidelines

Setup

BGPT is a remote MCP server — no local installation required. Configure it in your agent's MCP settings before use; this skill instructs the agent to call the search_papers MCP tool and does not enable MCP access by itself.

Claude Desktop / Claude Code

Add to your MCP configuration:

{
  "mcpServers": {
    "bgpt": {
      "command": "npx",
      "args": ["mcp-remote", "https://bgpt.pro/mcp/sse"]
    }
  }
}

npm (alternative)

npx bgpt-mcp

Usage

Once the BGPT MCP server is configured, call its search_papers tool via the agent's MCP interface (not via Bash):

Search for papers about: "CRISPR gene editing efficiency in human cells"

The server returns structured results including:

• Title, authors, journal, year, DOI
• Methods: Experimental techniques, models, protocols
• Results: Key findings with quantitative data
• Sample sizes: Number of subjects/samples
• Quality scores: Study quality assessments
• Conclusions: Author conclusions and implications

Pricing

• Free tier: 50 searches per network, no API key required
• Paid: $0.01 per result with an API key from bgpt.pro/mcp

Agent operating procedure

1. Check the environment. Confirm the research question, databases, date range and inclusion criteria.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run the search on one database with a narrow query and check that the results are relevant.
4. Execute the full task using the instructions and references above.
5. Validate the result. Every reference is retrieved from a real record with a resolvable identifier; counts and search strings are recorded.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
An API rate-limits or returns errors	Back off and retry, reduce the batch size, or switch database, and report the gap.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never summarize a paper you have not retrieved; never cite from memory.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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