analyze_clinical_caseCaso clínico → doenças candidatas + literatura relevante em 1 chamada. Apenas informativo, nunca diagnóstico.
| Name | Required | Description | Default |
|---|---|---|---|
| papers_limit | No | ||
| diseases_limit | No | ||
| case_description | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is known. The description adds valuable context beyond annotations with 'Apenas informativo, nunca diagnóstico', clearly stating the tool is not for diagnostic purposes. This is a meaningful behavioral disclosure.
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 short sentences with a clear visual arrow structure, front-loaded with the core purpose. Every word earns its place, and there is no filler.
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?
The tool has three parameters, no output schema, and moderate complexity. The description states the high-level output types and includes a safety disclaimer, but it doesn't explain the effect of the limit parameters or the nature of the returned literature and diseases. Adequate but with notable gaps.
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?
Schema description coverage is 0%, so the description must compensate. It only indirectly explains case_description via 'Caso clínico', but the limit parameters (papers_limit, diseases_limit) are not addressed in either the schema or the description. The parameter names are suggestive, but the description adds no clarity about how they affect the output.
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 uses a clear arrow structure 'Caso clínico → doenças candidatas + literatura relevante' which explicitly states the transformation from clinical case to two outputs. It distinguishes itself from sibling tools by emphasizing the combined result 'em 1 chamada', though it doesn't explicitly name 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 phrase 'em 1 chamada' implies this tool is used to get both diseases and literature in a single call, but no explicit when-to-use or when-not-to-use guidance is provided. The caveat 'Apenas informativo, nunca diagnóstico' offers a usage restriction but no alternative tool suggestions.
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 have clear, distinct purposes, but there is a cluster of 'find' tools (find_similar_diseases, find_phenotypically_similar, find_diseases_by_phenotypes) that could be confused; descriptions differentiate them (semantic vs HPO similarity vs exact match), and the paper search tools also differ by input type. Overall, ambiguous pairs are explicitly disambiguated, leaving only a few close calls.
All tools consistently follow a snake_case verb_noun pattern, using a limited set of verbs (analyze, explain, find, get, search) that map predictably to tool functionality. No mixed conventions or vague names are present, making the naming highly systematic.
At 20 tools, the server is slightly above the ideal range of 3-15, but each tool serves a distinct function within the rare disease knowledge platform. The breadth of features—search, similarity, detail, evidence, literature, hypotheses, graph exploration—justifies the count without feeling bloated or redundant.
The tool surface comprehensively covers the rare disease domain: search, differential diagnosis, disease detail, evidence, SUS/trials, reference centers, literature, hypotheses, and graph analytics. Write operations are not expected for a read-only knowledge base, and the inclusion of research log and recent updates closes all apparent gaps.