Top trending Kalshi events ranked by aggregate 24h volume across child markets. Filterable by category. Daily-briefing-ready output. Includes the highest-volume sub-market per event.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max events to return (default 25, max 50) | |
| category | No | Optional category filter (Elections, Politics, ...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses ranking methodology (aggregate 24h volume), the inclusion of the highest-volume sub-market per event, and categorical filtering. It does not detail output format or pagination, but the core behavioral traits are well explained.
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 three sentences long, front-loaded with the main purpose, and every sentence contributes useful information without redundancy or filler. It is tightly structured and easy to scan.
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?
Given the low complexity, no output schema, and two well-documented parameters, the description provides sufficient context: it explains what is returned (trending events with top sub-market), how ranking works, and filtering options. It could mention the output format explicitly, but it is not a significant gap for this use case.
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%, so the baseline is 3. The description only reiterates that the tool is filterable by category and does not add meaningful detail about the limit parameter beyond what the schema already provides.
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's function: ranking top trending Kalshi events by aggregate 24h volume across child markets. It distinguishes itself from siblings like pg_trending_markets by specifying Kalshi events and the sub-market inclusion behavior.
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 provides clear context for when to use the tool (for a top-trending Kalshi events overview, filterable by category, suitable for daily briefings). It does not explicitly mention alternatives or when-not scenarios, but the context is strong enough for an agent to select it appropriately.
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 has a clear, distinct purpose covering different aspects of prediction market integrity (market analysis, wallet analysis, AML/KYC, alerting, reporting). There is minimal overlap risk, as even related tools (e.g., pg_insider_signal_scan vs. pg_information_advantage_score) are differentiated by input (market vs. wallet) and output type.
All tools share the 'pg_' prefix and use descriptive snake_case names, making the set predictable. However, the verb/noun order is inconsistent (e.g., pg_whale_add vs. pg_market_details). The pattern is still clear and functional, so minor deviation from a strict verb_noun pattern.
With 33 tools, the set is large but well-scoped for a comprehensive platform covering market analysis, wallet intelligence, compliance, and reporting. Each tool serves a distinct function, and the count is justified by the breadth of the domain, though it pushes the upper bound of 'reasonable'.
The toolset covers the full lifecycle of prediction market integrity work: from market discovery and integrity scanning to wallet analysis, entity resolution, AML/KYC, watchlist management, alerting, and SAR reporting. There are no obvious gaps for the stated purpose.