get_literature_relationsRelações tipadas extraídas da literatura biomédica (associate/treat/cause/inhibit/stimulate/prevent) entre a doença e genes/fármacos, com nº de publicações como proveniência. Filtrável por predicado.
| Name | Required | Description | Default |
|---|---|---|---|
| orphaCode | Yes | ||
| predicate | No |
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | ||
| orphaCode | Yes | ||
| relations | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already supply readOnlyHint and openWorldHint, so the description's burden is lower. It adds that the tool returns typed relations with publication counts and is filterable by predicate, but it does not disclose behaviors like pagination, data update frequency, or result limits. This is modest extra value beyond annotations.
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?
The description is two concise sentences, front-loading the main function (typed relations extraction) and then adding the filter capability. No wasted words or repetition.
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?
Given the moderate tool complexity and the presence of an output schema and annotations, the description sufficiently covers the essential semantics: disease-to-gene/drug relations, predicate filtering, and publication counts as provenance. It leaves out minor details like specific predicates already in the schema, but the overall picture is complete for an agent.
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?
With 0% schema description coverage, the description must explain parameters. It implies orphaCode is a disease identifier and explains predicate as a filter, listing several predicate values. However, it omits two enum values from the schema and does not explicitly state that predicate is optional, so the coverage is incomplete.
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 clearly states the tool's function: it retrieves typed relations from biomedical literature between a disease and genes/drugs, specifying the predicate types and provenance (number of publications). This distinctly separates it from siblings like find_papers_for_disease or explain_relation, which focus on papers or explanations.
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?
No guidance is provided on when to use this tool versus alternatives. There is no comparison with sibling tools, no recommended context, and no exclusions. The only usage hint is 'filterable by predicate,' but that is a feature rather than a usage directive.
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.