Get what the prediction markets are betting on right now. Fetches top markets by volume/liquidity.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (default 10, max 50) | |
| source | No | polymarket, manifold, or all (default: all) | all |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 only states the basic behavior (fetches top markets by volume/liquidity) but lacks disclosure of traits like read-only nature, rate limits, pagination, or what exactly is returned (e.g., market summaries vs full details).
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 extremely concise with two sentences, front-loading the core purpose. Every word contributes value with no unnecessary 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?
For a simple tool with no output schema and only two parameters, the description is minimally adequate. However, it lacks details about the return format (e.g., is it a list of market IDs or summaries?) which could hinder an AI agent's understanding of the output.
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%, with both parameters (limit, source) already well-described in the schema. The description adds context about 'volume/liquidity' but does not significantly enhance parameter understanding beyond what the schema provides.
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 verb 'Get' and the resource 'what the prediction markets are betting on right now', and specifies the method 'Fetches top markets by volume/liquidity'. It distinguishes from sibling tools like 'browse_prediction_markets' and 'get_market_details' by focusing on trending predictions, though not explicitly.
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 does not provide any guidance on when to use this tool versus alternatives (e.g., 'browse_prediction_markets' for all markets or 'get_market_details' for specific ones). No when-to-use, when-not-to-use, or alternative context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.