term_children"More specific HPO phenotypes under [HP:N]" / "child terms of [phenotype]" — direct children of an HPO term in the ontology graph. Use to narrow from a general phenotype (e.g. "Abnormality of the nervous system") to specifics (seizures, ataxia, etc).
| 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=true, idempotentHint=true, destructiveHint=false, covering safety. Description adds no additional behavioral traits beyond purpose, which is acceptable but not enhanced.
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 sentences with an example, no wasted words. Front-loaded with key purpose and usage.
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 simple tool (1 param, robust annotations, has output schema), description is complete. No need for return value details as output schema exists.
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 has 0% description coverage, but description adds meaning: parameter is an HPO term ID (e.g., HP:0000118). Example clarifies format, though schema lacks any description. Description adequately compensates.
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?
Clearly states the tool returns direct children of a given HPO term, with an example. Distinguishes from term_descendants and term_parents by specifying 'direct children' and 'narrow from general phenotype'.
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?
Explicitly says 'Use to narrow from a general phenotype to specifics,' providing clear context. Does not explicitly mention alternatives or when not to use, but sibling tools and the description imply the appropriate use case.
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.