PubMed
Overview
Two complementary tools: existing query_pubmed_tool (keyword search, returns small JSON inline) + new download_pubmed_abstracts_by_pmids (batch-fetch full abstracts by PMID list, one efetch XML call, saves normalized JSON to disk).
Project Tools (VenusFactory2)
| Tool |
Args |
Returns |
Description |
| query_pubmed |
query (text), max_results (default 5, max 50), max_content_length |
JSON list of paper records inline |
Keyword search; small payload to agent. |
| download_pubmed_abstracts_by_pmids |
pmids (list of PMID strings, max 200 per call), out_path (JSON file path), timeout (default 60s) |
rich JSON envelope; saved file is {requested_pmids, articles[]} where each article has pmid, title, authors, journal, year, doi, abstract (structured abstracts join Label: body segments with newlines) |
Batch-fetch full abstracts + metadata by PMID via efetch. |
When to Use Each
| Goal |
Tool |
| "Find papers about X" |
query_pubmed |
| "Get me the full abstracts for these 50 PMIDs" |
download_pubmed_abstracts_by_pmids |
| Build a corpus for fine-tuning a biomedical NLP model |
query_pubmed → collect PMIDs → batch fetch via download_pubmed_abstracts_by_pmids |
| Resolve a citation string to a PMID |
(out of scope here — use the search tool with the citation text, parse hits) |
Output Schema for download_pubmed_abstracts_by_pmids
{
"requested_pmids": ["34265844", "37962427", ...],
"articles": [
{
"pmid": "34265844",
"title": "Highly accurate protein structure prediction with AlphaFold",
"authors": ["Jumper J", "Evans R", ...],
"journal": "Nature",
"year": "2021",
"doi": "10.1038/s41586-021-03819-2",
"abstract": "Proteins are essential to life..."
},
...
]
}
Structured abstracts (with BACKGROUND:/METHODS:/RESULTS:/CONCLUSIONS: sections) are joined with \n and each section is prefixed by its label.
Rate Limiting
- Without
NCBI_API_KEY: 3 req/s.
- With
NCBI_API_KEY (free; get at https://www.ncbi.nlm.nih.gov/account/settings/): 10 req/s.
download_pubmed_abstracts_by_pmids does ONE efetch for the whole batch — much friendlier than N individual fetches.- Hard cap: 200 PMIDs per call (split into chunks if you need more).
Common Mistakes
- PMID as int: the tool accepts strings; pass
"34265844", not 34265844. (Int is coerced to str internally but use string defensively.)
- >200 PMIDs in one call: returns
ValidationError. Split into chunks of ≤200.
- Missing abstract: not all PubMed records have abstracts (older / non-research / book chapters). The
abstract field will be null for those.
- Confusing PMID with PMCID: PMID is a number (e.g.
34265844); PMCID is PMC<number> (e.g. PMC8371605). This tool uses PMIDs.
References
1---2name: pubmed3description: PubMed — NCBI's biomedical literature database (>35M citations). Keyword search inline, or batch-fetch full title + structured abstract + authors + DOI for a known list of PMIDs. Use for medical / biological literature lookup, citation resolution, or building an abstract corpus for downstream NLP. Honors NCBI_API_KEY env var.4license: Unknown5---67# PubMed89## Overview1011Two complementary tools: existing `query_pubmed_tool` (keyword search, returns small JSON inline) + new `download_pubmed_abstracts_by_pmids` (batch-fetch full abstracts by PMID list, one efetch XML call, saves normalized JSON to disk).1213## Project Tools (VenusFactory2)1415| Tool | Args | Returns | Description |16|------|------|---------|--------------|17| **query_pubmed** | `query` (text), `max_results` (default 5, max 50), `max_content_length` | JSON list of paper records inline | Keyword search; small payload to agent. |18| **download_pubmed_abstracts_by_pmids** | `pmids` (list of PMID strings, max 200 per call), `out_path` (JSON file path), `timeout` (default 60s) | rich JSON envelope; saved file is `{requested_pmids, articles[]}` where each article has `pmid, title, authors, journal, year, doi, abstract` (structured abstracts join `Label: body` segments with newlines) | Batch-fetch full abstracts + metadata by PMID via efetch. |1920## When to Use Each2122| Goal | Tool |23|---|---|24| "Find papers about X" | `query_pubmed` |25| "Get me the full abstracts for these 50 PMIDs" | `download_pubmed_abstracts_by_pmids` |26| Build a corpus for fine-tuning a biomedical NLP model | `query_pubmed` → collect PMIDs → batch fetch via `download_pubmed_abstracts_by_pmids` |27| Resolve a citation string to a PMID | (out of scope here — use the search tool with the citation text, parse hits) |2829## Output Schema for `download_pubmed_abstracts_by_pmids`3031```json32{33 "requested_pmids": ["34265844", "37962427", ...],34 "articles": [35 {36 "pmid": "34265844",37 "title": "Highly accurate protein structure prediction with AlphaFold",38 "authors": ["Jumper J", "Evans R", ...],39 "journal": "Nature",40 "year": "2021",41 "doi": "10.1038/s41586-021-03819-2",42 "abstract": "Proteins are essential to life..."43 },44 ...45 ]46}47```4849Structured abstracts (with `BACKGROUND:`/`METHODS:`/`RESULTS:`/`CONCLUSIONS:` sections) are joined with `\n` and each section is prefixed by its label.5051## Rate Limiting5253- Without `NCBI_API_KEY`: 3 req/s.54- With `NCBI_API_KEY` (free; get at https://www.ncbi.nlm.nih.gov/account/settings/): 10 req/s.55- `download_pubmed_abstracts_by_pmids` does ONE efetch for the whole batch — much friendlier than N individual fetches.56- Hard cap: 200 PMIDs per call (split into chunks if you need more).5758## Common Mistakes5960- **PMID as int**: the tool accepts strings; pass `"34265844"`, not `34265844`. (Int is coerced to str internally but use string defensively.)61- **>200 PMIDs in one call**: returns `ValidationError`. Split into chunks of ≤200.62- **Missing abstract**: not all PubMed records have abstracts (older / non-research / book chapters). The `abstract` field will be `null` for those.63- **Confusing PMID with PMCID**: PMID is a number (e.g. `34265844`); PMCID is `PMC<number>` (e.g. `PMC8371605`). This tool uses PMIDs.6465## References6667- [PubMed E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25501/)68- [Get an NCBI API key](https://www.ncbi.nlm.nih.gov/account/settings/) (free)69- [PMID vs PMCID](https://www.nlm.nih.gov/bsd/policy/dla_FAQ.html)
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