bgpt-paper-search — Claude Code skill: what it does, how to install
Searches BGPT scientific papers by topic or DOI. Extracts full-text experimental evidence, reported statistics, and source provenance.
npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a claude-codeWhat bgpt-paper-search does
This skill queries the BGPT database via MCP to retrieve claim-level findings extracted from full paper texts rather than plain abstracts. Each returned record pairs an extracted finding with experimental parameters, author-reported limitations, statistics, and source excerpts. The underlying schema provides two tools: search_papers for discovery and lookup_paper for retrieving an exact DOI.
Installing the skill does not launch a local paper database. You must configure an MCP connection in your host using either HTTP (https://bgpt.pro/mcp/stream) or SSE (https://bgpt.pro/mcp/sse). If your host relies on a stdio bridge, mcp-remote via Node.js handles the connection. Free tier access works without credentials, while paid accounts require an Authorization: Bearer <key> header on the connection itself. Never pass credentials as tool arguments, since the tool schema has no parameter for keys.
Results follow provider V4 limits of five evidence units per article and up to two 320-character text snippets per unit. The search interface omits cursors, offsets, and Boolean grammar. If you need broader topic coverage, submit distinct query variants and deduplicate them manually by DOI.
What it helps with
- retrieve full-text experimental findings and statistics by topic
- extract claim-level evidence and methodology for a specific DOI
- build structured evidence tables with verifiable citation excerpts
- filter scientific literature by study design and publication recency
Example prompts for the agent
- Search for recent papers on CRISPR delivery neurons published in the last 90 days and show evidence records
- Fetch full-text evidence for DOI 10.1016/j.ymeth.2015.10.014 in evidence format
- Find five primary studies on gene expression in human cells with cited reference counts
How to install bgpt-paper-search
1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a claude-code2Manually: put the files in place
~/.claude/skills/bgpt-paper-search/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/bgpt-paper-search/
1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a codex2Manually: put the files in place
.agents/skills/bgpt-paper-search/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a cursor2Manually: put the files in place
.cursor/skills/bgpt-paper-search/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install K-Dense-AI/scientific-agent-skills bgpt-paper-search2Manually: put the files in place
.github/skills/bgpt-paper-search/SKILL.md1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a gemini-cli2Platform CLI
gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills --path skills/bgpt-paper-searchManually: put the files in place
.devin/skills/bgpt-paper-search/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about bgpt-paper-search
How to install bgpt-paper-search in Claude Code?
Run npx skills add K-Dense-AI/scientific-agent-skills --skill bgpt-paper-search -a claude-code in your terminal. The file will be placed in ~/.claude/skills/bgpt-paper-search/SKILL.md.
What does bgpt-paper-search do?
It connects your agent to BGPT to search scientific papers by topic or DOI. Instead of returning raw bibliographic records, it extracts central claims, statistical findings, and verifiable text passages from full papers.
How do I authenticate with the BGPT MCP server?
Free searches work without an API key. For paid access, set an Authorization: Bearer <key> header in your MCP client configuration; the tools themselves do not accept API keys as arguments.
Source text
SKILL.md · MIT
---
name: bgpt-paper-search
description: Searches BGPT scientific papers by topic or DOI and retrieves claim-level evidence extracted from full text, including experiments, reported statistics, scope, limitations, and provenance. Use for literature reviews, evidence synthesis, and finding experimental details beyond abstracts.
license: MIT
compatibility: Requires a configured remote BGPT MCP connection and internet access to bgpt.pro. A stdio bridge additionally needs Node.js/npm. Paid access requires a BGPT API key configured as an Authorization header.
metadata:
version: "1.3"
skill-author: BGPT
website: https://bgpt.pro/mcp/
last-reviewed: "2026-09-30"
---
# BGPT Paper Search
## When to use
Use BGPT to find full-text-derived evidence for a research question or a known
DOI. It returns extracted claims and their supporting experiments, rather than
only bibliographic metadata. Use these records to build a traceable evidence
table; they are not a complete systematic-review search or a validated
risk-of-bias assessment.
## Connect
Configure BGPT in the host before use; installing this skill does not register
an MCP server. The provider supports both transports:
| Transport | Endpoint |
| --- | --- |
| Streamable HTTP | `https://bgpt.pro/mcp/stream` |
| SSE | `https://bgpt.pro/mcp/sse` |
Use the host's native remote-MCP configuration when supported. For a host that
uses `mcpServers` JSON and requires a local stdio bridge, this configuration uses
`mcp-remote` (adapt it to the host's actual settings format):
```json
{
"mcpServers": {
"bgpt": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://bgpt.pro/mcp/stream", "--transport", "http-only"]
}
}
}
```
The published `bgpt-mcp` 1.1.0 npm package is a thin SSE wrapper around
`mcp-remote`, not a local paper database. Its bundled README predates the current
tool contract and pricing. Prefer the native connection or configurable bridge
above. The linked BGPT GitHub repository was unavailable at review time; use
[the current provider documentation](https://bgpt.pro/mcp/) and live tool schema.
Free access needs no key. For paid usage, configure `Authorization: Bearer <key>`
in the host's remote-server headers, using its credential storage. For the bridge,
[`mcp-remote` supports `--header-file`](https://github.com/punkpeye/mcp-remote#custom-headers)
with a local credential file. Do not put `api_key` in an MCP tool call: neither
current tool declares that argument. Authenticate through the connection.
## Search and DOI lookup
Discover the connected server's tools before calling them. The live BGPT 2.14.7
schema reviewed on 2026-09-30 exposes:
| Tool / argument | Contract |
| --- | --- |
| `search_papers.query` | Required string. Use short English search terms. |
| `num_results` | Integer, 1–100; default 16. Set it explicitly to control result use. |
| `days_back` | Optional integer; papers published within the last N days. Omit for all dates. |
| `min_citations` | Optional integer; minimum number of **references cited by the paper**, not citations received. |
| `study_type` | Optional string: `primary study`, `systematic review`, `meta-analysis`, `narrative review`, `protocol`, `dataset`, `commentary`, or `other`. |
| `output_format` | `evidence` (default), `full`, or `legacy`. |
| `lookup_paper.doi` | Required DOI string; also accepts `output_format`. A found paper counts as one result. |
Use the filter arguments instead of putting years or filters into the query.
No Boolean-query grammar, offset, page, or cursor is documented by this schema.
A result limit is not pagination: do not invent continuation arguments or claim
the search enumerates every matching paper. For broader coverage, run explicit
question variants and deduplicate by DOI while retaining the query history.
Search example verified with one public result (invoke through the connected
tool interface):
```json
{"query": "CRISPR human cells", "num_results": 1, "output_format": "full"}
```
Illustrative filtered search (filters were schema-checked, not executed):
```json
{"query": "CRISPR delivery neurons", "num_results": 5, "days_back": 90, "study_type": "primary study", "output_format": "evidence"}
```
For a paper already identified, call `lookup_paper` with its bare DOI rather
than hoping a title search returns that exact paper:
```json
{"doi": "10.1016/j.ymeth.2015.10.014", "output_format": "evidence"}
```
Do not interpret a connection, quota, or tool error as an empty evidence set.
The provider publishes a result allowance, but no numeric request-rate limit
on the reviewed page; avoid assuming unlimited request throughput.
## Read the result contract
Search returns an envelope with a `results` list and `count`; record the returned
`query` too, since the service may rewrite search terms. DOI lookup returns `found`
and a single `result`; handle not-found before reading it. Inspect the MCP
result for tool errors before interpreting the envelope.
- `evidence` gives compact evidence records; empty optional fields may be absent.
- `full` adds legacy paper metadata to the evidence record.
- `legacy` requests the older paper metadata representation. Do not assume every
paper has all previously advertised fields or a quality score.
The provider says evidence/full formats omit papers without extracted evidence.
However, the live smoke test returned a record with `schema_version: "legacy"`,
`extraction_status: "legacy"`, and an empty `evidence_units` list in both formats.
A returned record therefore does not guarantee claim-level provenance. An empty
search or unfound DOI also does not establish that the paper does not exist.
Retain `doi`, `title`, `publication_date`, and `publication_name` when available,
then inspect `central_claim` and `evidence.evidence_units`. Each unit links its
claim to an experiment, reported statistics, demonstrated scope, and provenance.
Keep the schema version: older records can have a different shape. Legacy full
metadata can contain nulls, numeric strings, and serialized text instead of
native lists; preserve the raw values and never execute them as code.
The provider documents V4 limits of five evidence units per record, two provenance
passages per unit, and 320 characters per passage. These are bounded extractions,
not the complete article. Check `extraction_status`, `normalization_warnings`,
and `evidence.record_provenance`, including truncation information, before synthesis.
## Evidence checks
For each extracted result, retain the DOI and source section, table, or figure.
Verify sample sizes, units, experimental arms, reported statistics, and uncertainty
against the paper before using them in a synthesis. Distinguish author-reported
limitations from extraction-generated interpretations. Preserve contradictory
or mixed evidence and demonstrated scope. If legacy quality scores are present,
use them only as screening aids, not as a study-specific risk-of-bias assessment.
## Allowance and review scope
At the review date the provider advertises 50 free **results** per network,
then $0.02 per returned result. A successful DOI lookup counts as one result;
unfound lookups cost none. Recheck the allowance and pricing before a large
batch. Do not repeatedly retry a quota-exhausted request.
Sources: [provider setup, record formats, and billing](https://bgpt.pro/mcp/),
[live MCP endpoint](https://bgpt.pro/mcp/stream),
[published BGPT wrapper metadata](https://registry.npmjs.org/bgpt-mcp/latest),
and [bridge documentation](https://github.com/punkpeye/mcp-remote).
The tool schemas, one-result full search, matching evidence-format DOI lookup,
and SSE handshake were checked with unauthenticated requests. The public
search/lookup returned a legacy-schema record, not a V4 fixture. Paid
authentication, filtered search behavior, and host-specific configuration were
not tested.
© K-Dense-AI · MIT · K-Dense-AI/scientific-agent-skills