Find questions that users are searching for but no prediction market exists. Useful for discovering new market creation opportunities or identifying emerging topics with real trading interest.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days (default 7) | |
| limit | No | Max results (default 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It does not disclose data freshness, pagination, response format, or any constraints beyond what the schema implies.
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?
Two concise sentences that front-load the core purpose and immediately follow with usage scenarios. No redundant or filler content.
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 low parameter count, full schema coverage, and no output schema, the description provides adequate context for basic invocation. Lacks details on result format but remains sufficient for initial understanding.
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% and both parameters (days, limit) have descriptions. The tool description adds no additional context beyond the schema, so 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?
Description clearly states 'Find questions that users are searching for but no prediction market exists' with a specific verb and resource. This distinguishes it from sibling tools like execute_url, get_arbitrage, etc., which serve different purposes.
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?
Description mentions it is 'useful for discovering new market creation opportunities or identifying emerging topics' which suggests when to use, but does not explicitly exclude use cases or name alternative tools.
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 clearly distinct purpose: deep link generation, arbitrage detection, historical data, live quotes, unmet demand discovery, and market routing. No two tools overlap in functionality.
All tool names follow a consistent verb_noun pattern with snake_case (execute_url, get_arbitrage, get_history, get_quote, get_unmet_demand, route_market), making them predictable and easy to differentiate.
Six tools is well-scoped for a prediction market surveillance and trading facilitation server. Each tool covers a distinct aspect of the domain without unnecessary bloat or insufficiency.
The tool set covers the full lifecycle from market discovery (route_market, get_arbitrage, get_unmet_demand) to data analysis (get_history, get_quote) to action (execute_url), with no obvious gaps given the server's stated purpose.
