Search Polymarket markets by keyword (e.g. 'election', 'fed', 'bitcoin'). Returns question, slug, 24h volume, liquidity, end date.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10, max 50) | |
| query | Yes | Keyword to search | |
| active_only | No | Only active / non-closed markets |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It lists output fields (question, slug, 24h volume, liquidity, end date) but does not mention default filtering like active_only or limit behavior, which are present in the schema. It sufficiently describes the core read-only search behavior.
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 two concise sentences, front-loaded with the primary verb and resource, and includes only essential information: examples of keywords and return fields. No wasted words.
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 (3 parameters, 100% schema coverage) and no output schema, the description sufficiently covers the tool's behavior and output. It could be slightly improved by noting the default active_only behavior, but the schema makes this visible to the agent.
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 has 100% coverage for all three parameters with clear descriptions. The tool description adds no additional parameter context, so the baseline score of 3 applies; it does not need to compensate for missing schema information.
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 the tool's function: 'Search Polymarket markets by keyword' with concrete examples. It differentiates from siblings like pg_kalshi_search by specifying the platform (Polymarket) and from pg_market_details/trending markets by focusing on keyword search.
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 examples of keywords, implying usage for searching by topic. However, it does not explicitly state when to use this tool versus alternatives like pg_trending_markets or pg_market_details, nor are any exclusions mentioned.
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.