swop_get_event_live_statusCheck whether an event (e.g. a game) is live, its current period and elapsed time, and whether it has closed. Never infer live status from the clock — use this.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Event slug |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish this is read-only, open-world, and non-destructive. The description adds meaningful behavioral detail: the tool returns current period, elapsed time, and closure state, and that it is authoritative over clock-based inference. This is sufficient context beyond the annotations.
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, front-loaded with what the tool does and what it returns, then a crisp usage rule. Every sentence earns its place with no redundant detail.
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?
The tool is simple (one parameter) and annotated as safe/read-only. Since there is no output schema, the description covers the key return concepts: live status, period, elapsed time, and closed state. It could name exact response fields or units, but the semantic coverage is adequate for correct invocation.
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%; the 'slug' parameter is already described as an 'Event slug'. The description only adds the example '(e.g. a game)', which slightly refines semantics but does not carry substantial new meaning beyond the schema.
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 ('Check whether') and clearly identifies the resource: live status of an event, including period, elapsed time, and closed state. It also distinguishes itself from sibling tools by emphasizing that live status should never be inferred from the clock, making its 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 gives explicit usage guidance: use this tool whenever live status is needed rather than inferring it from the clock. It does not explicitly list when-not-to-use or alternative tools, but the instruction 'use this' is strong and contextually clear.
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.
Every tool targets a distinct resource and action: search_markets is for discovery while get_event_markets is for a single event's full detail, and lookup_identity/search_identities, get_prices/get_price_history, and send/pay_x402_link are all clearly separated. There is no realistic pair of tools an agent would struggle to choose between.
All tools follow a consistent snake_case, swop_-prefixed, verb-first pattern such as get_*, search_*, create_*, update_*, send, and pay_*. Even slightly longer names like check_predictions_access stay within the same predictable structure.
21 tools is on the higher side, but the server covers several distinct clusters: prediction-market data, account/wallet info, identity resolution, SmartSite commerce, and payments. Each tool has a clear role, so the count is slightly over the ideal range but still reasonable for the apparent scope.
The market data and payment send flows are fairly complete, but lifecycle operations are missing: products can be created and listed but not updated or deleted, swaps are quote-only, and there is no prediction-market order execution tool. These are notable gaps, though they do not block the core send/pay and market-data workflows.