Tracked world events (situations, races, negotiations) the engine follows as named arcs — each with its entities, related market-question count, cross-domain mention volume, and first/last-seen dates. Optional query filters by name or entity.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many (default 20, max 50) | |
| query | No | Filter by event name or entity (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden; it details the event structure (entities, market-question count, across-domain mention volume, dates) and optional filters. However, it does not explicitly disclose read-only behavior, return format, or ordering/pagination details beyond the schema's limit parameter.
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 two sentences, directly states the core content and the available filter, with no redundant wording. It front-loads the purpose and avoids filler.
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?
No output schema exists, so the description compensates by enumerating what each event includes. It covers the main use case and filter capability, though it could be clearer about the response being a list and any default ordering.
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 schema already provides 100% coverage for both parameters (limit and query), so the baseline is 3. The description adds that 'query' filters by name or entity, which mirrors the schema and provides no additional syntax or format details.
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 identifies the tool as returning tracked world events, describing them as named arcs with entities and metrics, which distinguishes it from sibling tools like get_entity or get_record. The verb is implied via the tool name 'get', and the scope is well-defined.
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 implies it should be used to retrieve world events, but does not explicitly contrast it with any of the many sibling get_* tools. It gives context about events being engine-tracked arcs, so an agent can infer when to use it, but no explicit when/where-not guidance is provided.
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.
