SimpleFunctions

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

No annotations provided, so description carries full burden. Discloses read-only nature, no auth requirement, no rate limits, data volume (~9,700 markets), and output size (~800 tokens). Missing only data freshness/latency details for a perfect score.

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?

Three sentences, zero waste: 1) Core value proposition and data source, 2) Operational constraints, 3) Usage guidelines and alternatives. Front-loaded with the essential 'what' (calibrated world model, prediction markets).

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?

No output schema exists, but description compensates with output characteristics (~800 tokens, markdown/json formats mentioned) and data provenance. Covers all critical operational aspects (auth, rate limits) despite missing annotations.

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 has 100% description coverage with enums documented. Description mentions topic categories ('geopolitics, economics, tech') that loosely map to focus parameter values, but adds no syntax details beyond the schema. Baseline 3 appropriate when schema is self-documenting.

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?

Excellent specificity: 'Calibrated world model... distilled into ~800 tokens of real-money probabilities' defines the exact resource and scope. Distinguishes from siblings by contrasting with get_changes (deltas) and get_context (broader bundle).

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?

Explicit when-to-use: 'Use when you need a compact snapshot'. Clear alternatives named: 'use get_changes for deltas only, or get_context for the broader bundle'. Perfect sibling differentiation.

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