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

Beyond the readOnly/idempotent annotations, the description extensively discloses behavioral traits: resolver confidence levels that short-circuit analysis, closed-market status with fan-out suppression, illiquid wide-spread warnings, GDELT fallback with retry fields, and cancellation-rule risk parsing (e.g., refund_50_50 with EV impact). This goes far beyond the annotations and arms the agent with critical safety context.

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 long but meticulously structured with capitalized section labels (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, RESOLVER CONTRACT, SAFETY) and front-loads the core purpose and usage in the opening sentences. Every section provides distinct, useful detail for a complex tool; while dense, there is little filler and the organization aids parsing.

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?

With no output schema, the description must explain return values and behaviors, and it does comprehensively: result.market fields, result.analysis model probability/edge and 24h-move warning, result.evidence keying, resolver match fields and alternatives, parent_event extractor, news fallback fields, status codes, and cancellation-rule handling. It covers normal operation and numerous edge cases, making it effectively self-contained for an agent.

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 already covers all three parameters with clear descriptions (market accepts slug/URL/question text, depth quick vs thorough, include_raw default and behavior). The description strengthens the depth parameter with concrete fan-out examples (e.g., BTC bet → coingecko + fred + gdelt+gnews), but adds no new parameter-level semantics beyond what the schema provides. Since schema coverage is 100%, the baseline of 3 is appropriate.

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 a clear verb+resource statement: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It then specifies accepted inputs (slug, URL, question text) and distinct use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'), making the tool's purpose unmistakable and differentiating it from sibling Polymarket tools.

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 usage scenarios ('Use for...') and rich operational guidance: always inspect resolver confidence before trusting analysis, check cancellation rules before sizing bets, and expect blocking statuses for low-confidence or closed markets. It does not explicitly name alternative tools for 'when not to use', but the positive guidance and edge-case handling are strong and actionable.

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