Get full details of a PubMed article by its PMID.
Returns title, all authors, complete abstract, MeSH terms,
publication types, journal info, and DOI.
Args:
pmid: PubMed ID (e.g. '39142850').
| Name | Required | Description | Default |
|---|---|---|---|
| pmid | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description takes full responsibility for behavioral transparency. It lists the returned fields (title, authors, abstract, MeSH terms, publication types, journal info, DOI), giving a clear picture of what the tool outputs. It does not explicitly state that the operation is read-only, but this is implied by the nature of the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise: two short sentences plus a parameter definition. It is front-loaded with the main purpose, followed by return value details. Every sentence contributes useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (single parameter, retrieval operation) and the presence of an output schema, the description is complete. It describes the input, the output fields, and the overall behavior. No additional context is necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides no description for the 'pmid' parameter (0% coverage). The tool description compensates fully by defining the parameter and giving an example: 'pmid: PubMed ID (e.g. '39142850').' This adds essential meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get full details of a PubMed article by its PMID.' It uses a specific verb ('Get') and resource ('full details of a PubMed article'), and references the input (PMID). This distinguishes it from sibling tools like search_pubmed (which returns a list) or get_cited_by (which returns citations).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus the alternatives. It implies usage for retrieving detailed information on a single article, but no when-not or direct comparison is provided. The agent must infer usage from the tool name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose with no overlap: find_related_articles finds similar papers, get_article_detail retrieves metadata, get_cited_by finds citations, search_mesh searches vocabulary, and search_pubmed performs general searches. The descriptions reinforce these distinct roles, making misselection unlikely.
All tools follow a consistent verb_noun pattern with snake_case (e.g., find_related_articles, get_article_detail, search_pubmed). The naming is predictable and readable throughout, with no deviations in style or convention.
With 5 tools, the server is well-scoped for literature search and retrieval. Each tool earns its place by covering key workflows: searching, retrieving details, finding related/citing articles, and vocabulary support. This count is neither too sparse nor bloated for the domain.
The toolset covers core PubMed operations effectively: search, retrieve details, find related/citing articles, and vocabulary lookup. A minor gap is the lack of tools for advanced filtering (e.g., by date range or journal) or batch operations, but agents can work around this with query syntax.