Get Ontology
get_ontologyOntology metadata by id (e.g. "efo", "mondo", "go").
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_ontologyOntology metadata by id (e.g. "efo", "mondo", "go").
| 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 the tool as read-only, open-world, idempotent, and non-destructive. The description adds no further behavioral context such as return format or error behavior. It simply restates the resource type without disclosing additional traits.
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 compact sentence that places the core idea—'ontology metadata by id'—at the front. It is appropriately sized with no unnecessary words.
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 one-parameter read tool with a rich output schema and comprehensive annotations, the description is largely sufficient. However, it does not explicitly state what 'metadata' includes or clarify that the id refers to ontology names, so it leaves some ambiguity.
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 input schema only defines id as a required string with no description. The description compensates by giving concrete examples ('efo', 'mondo', 'go') that clarify the expected id format. This adds value beyond the schema, though it does not enumerate all valid values.
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 indicates the tool returns ontology metadata for a given id, with examples distinguishing it from list_ontologies (which lists all) and get_term (which retrieves terms within an ontology). It is specific and effectively distinguishes from siblings, though the verb is implied rather than explicit.
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?
Provides no explicit instructions on when to use this tool versus alternatives. The context implies it is for fetching a single ontology's metadata by id, but no alternatives or exclusions are mentioned. It offers only minimal guidance.
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.