Centros de referência
find_reference_centersCentros por doença e/ou UF, com CNES.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| orphaCode | No |
raras
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions that CNES is included in results, but gives no information about return format, filter behavior (whether filters are optional, combine with AND/OR), or data source specifics. The fragment is too sparse to inform an agent about side effects or data shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (six words) with no wasted text. It is a single compact phrase, which is efficient for conciseness, though the lack of sentence structure limits the amount of information conveyed. The brevity earns a high score for conciseness, but the content is not fully structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It does not explain what the tool returns (list vs. detail), how filters interact, what CNES is, or when to use it over similar lookup tools. The description gives only a vague context, leaving significant gaps for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description maps 'doença' to orphaCode and 'UF' to state, providing meaning that is not in the schema. However, 'limit' is not mentioned at all, and the schema has 0% description coverage. The partial explanation of two of three parameters adds moderate value but doesn't fully compensate for the missing limit semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Centros por doença e/ou UF, com CNES' identifies the resource (reference centers) and two filter dimensions (disease, UF), but lacks an explicit verb like 'find' or 'list'. It doesn't clearly distinguish from sibling search tools beyond implying filtering by rare disease code and state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of appropriate scenarios, required context, or how filters combine (e.g., AND/OR semantics). This is a bare statement of content, not usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
B3/5.0
Disambiguation4/5
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.
Naming Consistency5/5
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.
Tool Count4/5
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.
Completeness5/5
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.