Collect an async answer by ticketId (from ask). Free and unmetered — the work was paid for once at submit. status:'working' → poll again after pollAfter seconds; 'done' → the complete answer rides in this response; 'failed' or 'lost' → the reason, stated plainly, and asking again is the remedy.
| Name | Required | Description | Default |
|---|---|---|---|
| ticketId | Yes | The uuid the ask tool returned |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and succeeds. It discloses the cost model ('free and unmetered'), the asynchronous status lifecycle, the fact that 'done' includes the complete answer, and the action needed for failures. This is far beyond minimal disclosure.
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 compact and front-loaded: the first phrase states exactly what the tool does, and every subsequent clause earns its place by explaining polling behavior, cost, and terminal states. No filler or repetition.
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 single-parameter tool with no output schema, the description fully explains the integration point (from ask), the response states, the polling interval, the cost implication, and the failure remedy. The agent has everything needed to invoke and interpret the result correctly.
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 coverage is 100% because the only parameter, ticketId, is already described as 'The uuid the ask tool returned'. The description's reference to 'ticketId (from ask)' adds no new semantic detail beyond what the schema provides, so baseline 3 applies.
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 ('Collect') with a clear resource ('async answer by ticketId'), explicitly ties to the 'ask' tool, and differentiates itself from sibling get_* tools by focusing on asynchronous result retrieval. The status-based outcome states make the purpose unmistakable.
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?
It provides explicit conditional guidance: poll after pollAfter seconds when status is 'working', and treat the response as final when status is 'done'. For 'failed' or 'lost', it states the remedy ('asking again'), establishing when to use this tool versus re-submitting via ask.
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 targets a distinct function: ask is async event queries, get_answer retrieves results, get_signals and top_accelerating provide different signal views, get_entity and search_entities cover entity details and search, etc. No two tools have overlapping purposes, and descriptions clearly separate them.
The majority of tools follow a 'get_' prefix for retrieval (13 of 21) and other clear verbs like 'ask', 'create_order', 'propose_topic', 'send_feedback'. A few use noun phrases like 'todays_brief' and 'top_accelerating', which breaks the pattern slightly but are still intuitive. Overall consistent and predictable.
21 tools cover the full scope of the server: exploration, querying, ordering, account management, feedback, and data records. Each tool serves a clear purpose without redundancy, and the count feels well-scoped for the domain.
The tool surface covers all major workflows: browsing signals, asking questions, getting detailed reports, ordering, providing feedback, and proposing new topics. Minor gaps exist (e.g., no explicit tool to cancel an order or manage watched topics directly), but core operations are present and agents can handle the domain effectively.
