Bgpt Mcp

BGPT MCP — Scientific Paper Search with Structured Critical Appraisal

BGPT is a remote Model Context Protocol (MCP) server that searches a database of scientific papers built from full-text studies. Unlike typical search tools that return titles and abstracts, BGPT extracts raw experimental data along with 25+ structured fields per paper.

When to Use

Use BGPT whenever the user asks to:

• Find scientific papers on a topic
• Evaluate research quality, reproducibility, or COI
• Surface limitations, blindspots, or falsifiability criteria of studies
• Retrieve methods, sample sizes, populations, or experimental techniques
• Build an evidence-grounded answer that requires citing primary literature
• Compare evidence across multiple studies with quality signals

Setup

Add BGPT to the MCP client config — no API key needed for the free tier (50 free results, then $0.02/result).

Claude Desktop / Cursor / any MCP client (SSE):

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

Streamable HTTP (alternative): https://bgpt.pro/mcp/stream

Tool: search_papers

The single tool exposed by BGPT.

Input:

• query (string, required) — natural-language description of what to search for. Examples: "CRISPR base editing in vivo delivery 2024", "semaglutide cardiovascular outcomes", "transformer attention sparse activations".

Output (per paper):

Each result is a paper with these structured fields:

• title, authors, journal, year, doi, url
• abstract
• methods_and_experimental_techniques
• results_and_conclusions
• sample_size_and_population
• study_context
• paper_limitations_and_biases
• study_blindspots — confounders the authors did not examine
• how_to_falsify — what evidence would disprove the paper's claims
• conflict_of_interest
• funding_json
• data_availability_statements
• code_and_data_links
• quality_scores — calibrated 0-10 scores for: scientific rigor, novelty, generality, usefulness, reproducibility, depth

Best Practices

1. Cite the structured fields, not just the abstract. When summarizing a paper, surface the paper_limitations_and_biases, study_blindspots, and conflict_of_interest fields in the response so the user gets calibrated context. 2. Use `quality_scores.reproducibility` to triage. When multiple papers conflict, prefer the higher reproducibility score and note the difference. 3. For systematic reviews and clinical questions, leverage sample_size_and_population to filter underpowered studies. 4. For methods-heavy questions (replication, technique comparison), focus on methods_and_experimental_techniques. 5. Always show `how_to_falsify` when the user asks "is X true?" — the falsification criterion is the test the claim should pass. 6. Always disclose `conflict_of_interest` when summarizing pharma, nutrition, or industry-funded research.

Example Workflows

Evidence-graded literature search

User: "What's the evidence for time-restricted eating on cardiovascular outcomes?"

Use search_papers("time-restricted eating cardiovascular outcomes RCT"). From results, return a comparative table that includes for each paper: sample size, primary outcome, reproducibility score, and disclosed COI/funding.

Critical appraisal

User: "Is paper X reliable?"

Search for the paper title. Surface paper_limitations_and_biases, study_blindspots, how_to_falsify, and quality_scores. State the falsification criterion explicitly.

Replicability check

User: "Has this finding replicated?"

Search the original finding, then search for follow-up replications. Compare quality_scores.reproducibility across studies and surface methodological differences from methods_and_experimental_techniques.

Pricing

• 50 free searches per network (no API key needed)
• $0.02 per result thereafter (API key from https://bgpt.pro/mcp)

Links

• Site: https://bgpt.pro/mcp
• npm: https://www.npmjs.com/package/bgpt-mcp
• Source: https://github.com/connerlambden/bgpt-mcp