get_probability

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's function and data sources (Kalshi and Polymarket), but lacks details on rate limits, authentication requirements, error handling, or response format specifics. It provides basic context but misses deeper behavioral traits.

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 and front-loaded with core functionality, followed by usage guidelines and examples. While slightly longer due to examples, every sentence adds value by clarifying scope and application, avoiding redundancy.

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 moderate complexity (2 parameters, no output schema, no annotations), the description provides good contextual completeness. It covers purpose, usage, data sources, and examples, though it could benefit from more detail on response structure or error cases to fully compensate for the lack of output schema.

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 description coverage is 100%, so the schema already documents both parameters (market_id and query). The description doesn't add specific parameter semantics beyond implying that 'query' can be a natural language search, which is already covered in the schema. This meets the baseline for high schema coverage.

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's purpose with specific verbs ('Get real-time probability, odds, and likelihood') and resources ('prediction market outcome'), distinguishing it from siblings like get_history or get_sentiment by focusing on current probability data rather than historical trends or sentiment 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 description provides explicit guidance on when to use this tool ('when asked about chances, odds, likelihood, forecasts, or predictions for any event') and includes specific examples across domains (elections, crypto, sports, etc.), clearly differentiating it from alternative tools like list_markets or search_markets that might handle broader market discovery.

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