ask_public_knowledgeAnswer questions about Helena Bioinformatics and its products from approved public sources, with citations and explicit limits. May return evidence only.
| Name | Required | Description | Default |
|---|---|---|---|
| site | No | all | |
| history | No | ||
| language | No | ||
| question | Yes | ||
| request_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
| protocol_metadata | Yes | ||
| canonical_response | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context: 'May return evidence only' and 'explicit limits.' This goes beyond annotations without contradicting them, though it doesn't detail what the limits or evidence-only responses mean.
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 sentences, front-loaded with the action verb 'Answer,' and contains no redundant or filler content. Every phrase adds value (scope, sources, citations, limits).
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?
Despite having output schema and annotations, the description leaves significant gaps: parameter semantics are entirely unexplained, 'explicit limits' is vague, and there is no mention of the site parameter (evidence, helena, folklore, all) or history/language options. This is insufficient for a 5-parameter tool with 0% schema description coverage.
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 must compensate for parameter meaning, but it does not. None of the five parameters (site, history, language, request_id, question) are mentioned or explained, leaving the agent to infer semantics solely from names and enums.
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 answers questions about Helena Bioinformatics and its products from approved public sources, with specific verb 'answer' and resource 'questions about Helena Bioinformatics'. It distinguishes from sibling by emphasizing citations and explicit limits, but doesn't explicitly name or contrast with search_public_knowledge.
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 implies usage for factual questions requiring citations ('with citations and explicit limits'), but provides no explicit when-to-use vs alternatives or exclusions. The sibling search_public_knowledge is not referenced, so the guidance is only implied, not explicit.
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.
Each tool has a clearly distinct purpose: ask_public_knowledge answers questions, search_public_knowledge retrieves cited evidence, and support_helena handles a separate opt-in action about supporting Helena. No overlap or ambiguity between them.
All tools follow a consistent verb_noun pattern: ask_public_knowledge, search_public_knowledge, support_helena. The style is uniform and intuitive, making the set predictable.
Three tools is a well-scoped count for a knowledge-answering server. Each tool earns its place: one for querying, one for searching, and one for a distinct support action. No bloat or thinness.
The server's purpose is to provide answers from public knowledge with citations. The ask and search tools cover the core workflows fully, and the support tool addresses a separate but relevant action. No obvious gaps in the domain.