Adverse media scan — negative news on fraud, money laundering, corruption, sanctions. Delegates to AMLOracle. Core building block for evidence chains that link a wallet or trader to publicly reported concerns (always cite the source, never attribute).
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Optional language (e.g. en, de) | |
| name | Yes | Person or entity name | |
| limit | No | Max results (default upstream) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It reveals a key behavioral trait: delegation to AMLOracle, and implies results include sources to cite. However, it does not disclose whether the operation is read-only, potential data sensitivity, rate limits, or any side effects. This is a moderate level of transparency but not comprehensive.
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, front-loaded with the primary purpose. Each sentence earns its place: what it scans, how it works (AMLOracle), and usage guidance. No redundancy or 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?
The description covers purpose, delegation, and user guidance, which is solid for a tool with three simple parameters. However, it does not describe the output format or result presentation, and with no output schema, that would be helpful. Overall, it is mostly complete but not exhaustive.
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 description coverage is 100% (name, lang, limit all have descriptions). The tool description itself does not add semantics beyond the schema, simply framing the operation as a 'scan' by name. With full schema coverage, a baseline 3 is appropriate.
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 an adverse media scan for negative news on specific crime types (fraud, money laundering, corruption, sanctions), which distinguishes it from siblings focused on PEP checks or sanctions screening. However, it does not explicitly compare itself to those adjacent tools, so it misses the full sibling differentiation.
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 states it is a 'core building block for evidence chains' linking a wallet or trader to publicly reported concerns, giving a clear when-to-use context. It also provides a usage rule ('always cite the source, never attribute'). It does not explicitly say when not to use it or name alternatives, but the context is sufficient for an agent to infer appropriate usage.
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.