Find articles related to a given PubMed article.
Uses NCBI's computed similarity to find conceptually related
papers. Useful for literature review expansion.
Args:
pmid: PubMed ID of the source article.
limit: Maximum related articles to return (default 10, max 50).
| Name | Required | Description | Default |
|---|---|---|---|
| pmid | Yes | ||
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the use of NCBI's computed similarity and the limit parameter's default and max, but does not disclose whether the operation is read-only, rate limits, or error handling. Partially transparent.
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 concise, with two sentences of purpose and a clear parameter list. It is front-loaded with the main action and uses no unnecessary words.
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 (2 parameters, output schema exists), the description covers the core functionality and parameter behavior. It could mention what happens on invalid input, but is generally complete.
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 description adds meaning beyond the schema by clarifying that 'pmid' is the PubMed ID of the source article and specifying that 'limit' has a default of 10 and max of 50. Schema coverage is 0%, so this context is essential.
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 finds articles related to a given PubMed article using NCBI's computed similarity. It distinguishes from siblings like get_cited_by and search_pubmed, but could be more explicit about when to use this vs. alternatives.
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 mentions it is useful for literature review expansion, which implies a use case, but does not explicitly state when to use this tool over siblings, nor does it provide any exclusion criteria.
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.