get_recent_updatesDoenças cujo gêmeo digital ganhou evidência nova (autoria, PubTator3, Open Targets, verificação) nos últimos N dias. Faça polling para acompanhar mudanças (substituto stateless de subscriptions).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
| updates | Yes | ||
| sinceDays | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds behavioral context: it lists the types of evidence that count as updates and highlights the stateless polling nature. This goes beyond the annotations by clarifying the criteria for inclusion and the non-persistent behavior.
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 concise sentences: the first states the core purpose, the second explains the usage as a polling substitute. Every word contributes value, and the main function is front-loaded.
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?
For a simple tool with two optional parameters, an output schema, and read-only annotation, the description adequately covers purpose, usage, and parameter context. Minor gaps like sorting or pagination details are not essential given the output schema.
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 is the only source for parameter meaning. It explicitly mentions 'N dias' corresponding to the 'days' parameter, giving context for the time window. However, it does not explain 'limit' beyond its name, leaving minimal semantic assistance for that parameter.
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 specifies the tool's purpose: listing diseases whose digital twin has gained new evidence (authorship, PubTator3, Open Targets, verification) within N days. This names the resource (disease twins) and the action (recent updates), distinguishing it from sibling tools like get_disease_detail or get_evidence.
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 description explicitly instructs using this tool for polling to track changes, calling it a stateless substitute for subscriptions. This provides clear when-to-use guidance, though it does not explicitly name alternatives or exclusions.
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.