screen_markets

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

With no annotations provided, the description carries the full burden. It effectively discloses the tool's behavioral output by specifying that it returns 'scored markets with an action recommendation' and explicitly lists the three possible recommendation values ('analyze', 'watch', 'skip'). This is crucial behavioral context for agent decision-making, though operational details like rate limits or caching are absent.

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 well-structured with a clear purpose statement, return value description, and a dedicated Args section. Information is front-loaded with the core function and output type. The Args section is efficiently formatted without redundancy, though it occupies more space than a high-schema-coverage tool would require.

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 presence of an output schema, the description appropriately focuses on parameter documentation and high-level behavioral output rather than detailed return structure. With 5 parameters and complex filtering logic, the description provides sufficient context for invocation by documenting all filters and the recommendation system, though it could note whether the operation is read-only.

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% description coverage, requiring the description to compensate fully. The Args section successfully documents all 5 parameters (market_ids, platform, min_volume_24h, min_score, limit) with sufficient semantics, including valid enum values for platform ('kalshi', 'polymarket', or ''). This fully compensates for the schema deficiency.

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 performs 'batch screening' of markets using score, volume, or specific IDs as criteria. It further distinguishes itself from sibling tools by specifying that it returns markets with action recommendations ('analyze', 'watch', or 'skip'), which implies a filtering/validation function distinct from simple listing or deep analysis.

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 term 'Batch screen' implies the tool is for bulk filtering operations, and the parameter descriptions suggest when to use specific filters (e.g., min_volume_24h). However, it lacks explicit guidance on when to choose this over siblings like search_markets, list_markets, or analyze_market, leaving the selection criteria somewhat implied.

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