Term Children
term_childrenDirect children of a term.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| iri | Yes | ||
| ontology | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| _embedded | No |
term_childrenDirect children of a term.
| Name | Required | Description | Default |
|---|---|---|---|
| iri | Yes | ||
| ontology | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| _embedded | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive. The description adds the behavioral detail that it returns only direct children, not descendants, which is useful. However, it does not disclose behavior for edge cases such as terms with no children or invalid IRIs.
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 extremely concise with no filler, but it is under-specified. It reads more like a title or subtitle than a functional description, and its brevity does not compensate for the lack of parameter details.
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 read-only tool with an output schema and strong annotations, the core behavior is mostly captured by 'Direct children of a term.' However, the lack of any usage context or parameter explanation leaves meaningful gaps, making it only minimally adequate.
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%, and the description does not explain the 'ontology' and 'iri' parameters. The parameter names are self-explanatory to some degree, but the description adds no additional meaning about formats, examples, or constraints beyond what the schema's example shows.
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 'Direct children of a term' conveys the resource and scope, but it is a noun phrase rather than a clear verb-driven statement. It adds the 'direct' qualifier, which distinguishes it from term_ancestors, but it still largely restates the tool name 'Term Children' without explicitly noting that it returns or lists results.
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?
No guidance is provided about when to use this tool versus siblings like term_ancestors or get_term. The description does not mention traversal scenarios, prerequisites, or any exclusions, leaving the agent to infer usage from the name alone.
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 overlapping purposes, especially among Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded), betting research tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread), and memory tools (remember, recall, forget). An agent could easily select the wrong tool. Additionally, tools like 'discover_tools', 'search', and 'search_within' have unclear boundaries.
Most tool names use snake_case (e.g., 'entity_profile', 'validate_claim'), but there are inconsistencies with single-word verbs like 'forget', 'recall', 'remember', 'subscribe', 'unsubscribe', and the mixed pattern of 'ask_pipeworx' vs 'pipeworx_feedback'. Overall, the naming is somewhat consistent but not fully predictable.
With 32 tools, the server has a high but not extreme count. However, the tools span multiple unrelated domains (ontologies, financial data, betting, memory, subscriptions, AI visibility), making the server feel like a collection of disparate features rather than a focused toolset. This reduces the appropriateness of the count for a single server.
The tool surface has significant gaps. For example, ontology tools lack create/update/delete operations; betting tools only provide research and analysis but no placement; memory tools allow save/recall/delete but not update; and there is no tool for user authentication or account management despite subscription features. The server covers many areas but none completely.