Search active Polymarket markets with a free-text query and view relevant prediction markets, live odds, and links.
Free-text search over active Polymarket markets (lenient matching).
Use for questions like 'what markets exist about the Fed' — returns the most relevant active markets with live odds and links.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It discloses that matching is lenient, only active markets are returned, and results include live odds and links. This gives the agent a useful behavioral model beyond the bare operation name.
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 focused sentences: behavior first, then a concrete usage example. Every clause contributes useful information, and there is no repetition 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 behavior and use case are well described, and an output schema exists, so return-value details are not necessary. However, the top_k parameter is left undocumented in both the schema and the description, creating a minor but real gap for correct invocation.
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 0% property description coverage, so the description must compensate. It indirectly explains query through 'questions like...' but never explains top_k, its meaning, or how it controls the number of results. An agent must infer this from the parameter name and default value.
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 opens with 'Free-text search over active Polymarket markets', giving a specific verb and resource. It also adds 'lenient matching' and clarifies output as 'most relevant active markets with live odds and links', which distinguishes it from sibling tools like trending_markets, get_market, and match_news.
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?
It explicitly says 'Use for questions like...' and provides a concrete example ('what markets exist about the Fed'), making the intended use case clear. It does not name alternatives or exclusions, but the context is strong enough for an agent to know when to apply it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.