Papers de uma doença
find_papers_for_diseaseLiteratura relacionada a uma doença via embeddings (:SEMANTIC_MATCH, fallback vetorial).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| min_score | No | ||
| orphaCode | Yes |
raras
TDQS
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden. It only mentions the retrieval method (embeddings, semantic match) but doesn't disclose output format, ranking behavior, score thresholds, or read-only nature. Minimal behavioral insight beyond the name.
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 a single concise sentence, front-loaded with the core purpose. It is efficient and structurally clear, though overly terse, which limits its informational value.
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?
With no annotations, no output schema, and 3 parameters, the description is too sparse to be complete. It doesn't cover return values, parameter usage, or how to choose this over sibling tools, leaving the agent under-informed for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 explain any parameters. The disease input (orphaCode) is implied, but limit and min_score are left unexplained. The description adds no value over the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it finds literature related to a disease using embeddings (:SEMANTIC_MATCH, fallback vetorial). The verb 'find' and resource 'literature' are clear, and the disease context is implied. It doesn't explicitly distinguish from sibling search_papers_semantic, but the 'for_disease' scope adds differentiation.
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. It doesn't mention when not to use it or which sibling tools serve different purposes (e.g., search_papers_semantic for general paper search).
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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Glama MCP Gateway
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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.