Scan all open Kalshi markets and flag those that are manipulation-vulnerable: closing soon (<24h), low open interest, low liquidity, wide spread, low volume. Daily triage tool. Returns markets ranked by risk_score (≥30 threshold). Best paired with pg_kalshi_resolution_calendar for full compliance briefing.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category filter | |
| max_results | No | Max flagged markets (default 25, max 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the specific vulnerability factors, the output ranking by risk_score, and the ≥30 threshold. It does not mention auth, rate limits, or data freshness, but as a read-only scanning tool these are less critical; the disclosure is substantive.
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 core purpose and criteria, and contains no redundant or filler content. Every sentence contributes meaning, making it easy to parse quickly.
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 no output schema, the description clarifies that results are ranked by risk_score and filtered by threshold, which is essential output context. It also lists the key vulnerability dimensions, making the tool self-explanatory for a scan. Minor gaps include not specifying the exact output fields or any data-source caveats, but the description is adequate for selecting and invoking the tool.
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 already provides descriptions for both parameters (category filter and max_results, including default and max). The description adds no additional parameter-level context, so the baseline 3 applies for full schema coverage.
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 ('Scan') and resource ('all open Kalshi markets'), and clearly states the tool's function: flag manipulation-vulnerable markets based on concrete criteria (closing soon, low open interest, low liquidity, wide spread, low volume). It distinguishes itself from sibling tools by naming the risk_score threshold and its triage role.
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 phrase 'Daily triage tool' provides clear timing/context for use. It also suggests a complementary tool ('pg_kalshi_resolution_calendar') for a broader compliance briefing, which helps orient the agent. However, it does not explicitly state when not to use this tool or list alternative scans to prefer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.