Volume anomaly heuristic: compares 24h volume vs average daily volume since market start (z-like ratio) and flags thin-book situations where 24h volume >> on-book liquidity.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| slug | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the heuristic logic (comparing volumes, z-like ratio) and the flagging condition. However, it does not explicitly state whether the tool is read-only, what it returns, or any potential side effects. This is a moderate level of transparency.
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 a single, concise sentence that packs in the purpose and heuristic details without wasted words. It is front-loaded with the tool's primary function and structured clearly with a colon and conjunction.
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 has two optional parameters and no output schema. The description explains the core algorithm but does not explain how parameters are used or what the output looks like. It also lacks context about the meaning of 'on-book liquidity' or the expected result format, making it incomplete for an agent to use effectively.
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 defines 'id' and 'slug', but the description does not mention either parameter. With schema description coverage at 0%, the description should compensate by explaining how parameters identify the market or affect the heuristic, but it is completely silent. This leaves the agent with no guidance on what to provide.
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 states what the tool does: it compares 24h volume to average daily volume since market start and flags thin-book situations. It uses specific verbs ('compares', 'flags') and identifies the resource (market volume) and the heuristic approach (z-like ratio), which distinguishes it from sibling tools like pg_kalshi_thin_market_alert.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention any use case, prerequisites, or contrast with sibling tools. It only describes the tool's behavior without any context for selection.
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.