get_research_logRegistro cronológico do que o agente autônomo fez por esta doença: autoria, mineração de literatura, evidência genética, verificação, prospecção de fontes, recomputação de relações. Prova de que o gêmeo digital estuda a doença continuamente.
| Name | Required | Description | Default |
|---|---|---|---|
| orphaCode | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| events | Yes | ||
| orphaCode | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful context about the content (agent's actions, chronological order). However, it does not disclose behavioral nuances like pagination, potential size of the log, or that the log may be empty for new diseases. It does not contradict annotations; the added value is moderate.
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 a single sentence, front-loaded with the core concept 'Registro cronológico' (chronological record). It lists relevant content areas and a purpose statement, all without unnecessary verbosity. Every phrase adds value.
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 that an output schema exists to describe the return format, the description provides enough context about what the log contains and its purpose. It covers the agent's activities and the chronological aspect. It could mention ordering direction or absence of filtering, but such details are likely in the output schema or not essential for this simple one-parameter tool.
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?
The schema has one required parameter, orphaCode, with 0% description coverage. The description mentions 'por esta doença' (for this disease), which implicitly ties the parameter to a disease identifier, but it does not explicitly define orphaCode's meaning or format. Since the parameter name is self-explanatory and the description gives a hint, the gap is partially compensated but not fully.
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 explicitly states that the tool provides a chronological record of what the autonomous agent did for a specific disease, listing the types of activities (e.g., authorship, literature mining, genetic evidence). This clearly identifies the resource and distinguishes it from sibling tools that focus on disease data or evidence rather than the agent's research process.
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 does not specify when to use this tool over alternatives. It implies the log serves as proof of the digital twin's study, but it does not provide explicit context, such as 'use this to audit agent activity' or 'for actual evidence, use get_evidence.' No exclusions or alternatives are mentioned.
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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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.