Search PubMed for biomedical literature. Returns PMID, title, abstract, authors, journal, and DOI. 36M+ articles indexed.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | PubMed search query (e.g. "GLP-1 cardiovascular outcomes clinical trial") | |
| maxResults | No | Max articles (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided; the description adds context about return fields and index size (36M+ articles). However, it lacks disclosure of rate limits, authentication needs, or handling of large queries.
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?
Two concise sentences: first states purpose, second lists return fields and scale. No unnecessary words, front-loaded with key information.
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?
For a simple search tool with no output schema or nested objects, the description covers purpose, return fields, and index size. It lacks behavioral details like pagination or result limits, but is moderately 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?
Input schema has 100% description coverage; parameters are well-described. The tool description does not add extra meaning beyond the schema, so baseline score is 3.
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 tool clearly states it searches PubMed for biomedical literature and lists return fields (PMID, title, etc.). It is distinct from siblings like search_arxiv and search_nih_grants, but does not explicitly differentiate.
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 implies usage for biomedical literature searches with an example query, but provides no guidance on when to use this tool versus alternatives like search_arxiv (preprints) or search_nih_grants (grants).
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.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
