Search prediction markets across all venues

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and non-destructive behavior. The description adds meaningful context: 'Free public search' and 'No API key required' disclose access requirements, and the explanation of quality/blocking (blocked markets stay visible but cannot drive paper opens or alerts) reveals behavioral consequences beyond the annotations. This enriches the agent's understanding of what the tool can and cannot do.

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 lengthy but well-structured: it opens with the primary purpose, then covers return fields, special metadata, and usage guidance. While some sentences are dense, each portion earns its place given the tool's complexity. It is front-loaded and avoids unnecessary filler, though it could be trimmed slightly without losing clarity.

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 tool's complexity (12 venues, multiple return fields, cross-venue metadata) and the presence of a full output schema, the description is thorough. It explains return fields, reference probabilities, quality/blocking semantics, cross-platform links, and usage boundaries. The context is complete for an agent to decide when and how to invoke this tool.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description does not add additional parameter-level detail beyond the schema; it focuses on output fields and usage. It lists venue names, but these are already in the schema's source parameter description. Thus, the description adds no extra semantic value for the parameters.

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 a specific verb and resource: a free public search over prediction-market events across all 12 venues. It explicitly distinguishes itself from sibling discover_pm_markets by noting the broader venue scope, and also recommends pm_data_event for all outcomes. This leaves no ambiguity about what the tool does.

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 explicit when-to-use guidance: broader than discover_pm_markets, use pm_data_event for all outcomes and full evidence, and for trading use discover_pm_markets + pm_quote instead. It clearly delineates the appropriate context versus alternatives, and even states 'Research/data only.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.