term"What does HP:[N] mean" / "look up HPO phenotype [ID]" — fetch a single Human Phenotype Ontology (HPO) term by ID. HPO is the standard ontology for clinical phenotypes used in rare-disease research. Returns label, definition, synonyms, cross-references. Example ID: HP:0001250 (Seizure).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by stating return fields (label, definition, synonyms, cross-references), which is consistent and not redundant.
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?
Three concise sentences with front-loaded natural language query. Every sentence adds value: query pattern, purpose, ontology context, return fields, and example ID.
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 simple input (one string with examples) and the presence of an output schema (not shown but indicated), the description covers all needed context: input format, output contents, and domain context.
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 single parameter 'id' is fully explained in the description with format (HP:number) and example (HP:0001250). Schema coverage is 0% but irrelevant since the description compensates completely.
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 uses a specific verb ('fetch') and resource ('single Human Phenotype Ontology term'), clearly distinguishing it from sibling tools like term_children or term_parents. Examples and ontology context reinforce purpose.
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 provides natural language query examples ("What does HP:[N] mean") and specifies the domain (HPO for clinical phenotypes). It lacks explicit 'when not to use' guidance, but the single-term lookup is clear given siblings for hierarchies.
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.
Many tools have distinct purposes, but there is overlap among the Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread) and between ai_visibility_check and scan_competitor_ai_presence. Detailed descriptions help, but some tools could still be confused.
All tool names follow a consistent verb_noun snake_case pattern (e.g., ai_visibility_check, resolve_entity, validate_claim). No mixing of conventions.
With 27 tools spanning HPO, data queries, betting, memory, and utilities, the server is over-scoped. It aggregates multiple domains that would be better split into separate servers. The count feels excessive for a coherent set.
Within each domain (HPO ontology, Pipeworx data, Polymarket betting, etc.), the tool surface is reasonably complete. However, the overall server lacks a single clear purpose, making it hard to assess completeness holistically.