Doenças similares
find_similar_diseasesVector similarity via embeddings SOTA.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| orphaCode | Yes |
raras
TDQS
D1.8/5.0
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral transparency, yet it only mentions an embedding technique and the ambiguous acronym 'SOTA'. It does not disclose whether the operation is read-only, how the input is used, what the output contains, or any constraints, providing essentially no behavioral information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than good conciseness. 'Vector similarity via embeddings SOTA' is cryptic and does not effectively communicate the tool's function in a structured way.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool with no output schema and no annotations, the description is severely incomplete. It does not explain what the tool returns, how similar diseases are scored, or provide any context for interpreting results, making it impossible for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not compensate by explaining orphaCode or limit. The agent is left with only parameter names and basic constraints, making it difficult to understand what values are appropriate or how they affect the search.
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 'Vector similarity via embeddings SOTA' is vague and method-oriented rather than a clear statement of the tool's purpose. It does not explicitly say that the tool finds similar diseases, leaving the agent to infer from the name. It also fails to distinguish the tool from siblings like find_phenotypically_similar, which likely uses a similar vector-based approach.
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?
The description offers no guidance on when to use this tool versus alternatives such as find_phenotypically_similar or search_diseases. It does not mention the input orphaCode, typical use cases, or any exclusions, leaving the agent without context for tool selection.
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.