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

The description reveals numerous behavioral details beyond the simple annotations, including response shapes, resolver contract, fallback mechanisms, cancellation rule parsing, and safety short-circuits. It even warns about potential edge cases like wide spreads and resolution rules. Annotations confirm read-only, idempotent, open-world behavior, and the description aligns perfectly without contradictions.

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 well-structured with clear section headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). Every sentence adds value, covering necessary intricacies. However, it could benefit from slight trimming to improve readability while retaining critical details.

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 complexity (3 parameters, no output schema), the description provides comprehensive context on usage, expected outcomes, edge cases, and safety measures. It covers resolver contract, parent event extraction, fallback handling, and resolution rules, leaving no major gaps for an agent to understand how to invoke and interpret results.

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 coverage is 100%, so the baseline is 3. The description does not add significant meaning beyond the schema's own descriptions for the three parameters. While the extensive context indirectly informs usage, no additional parameter-level details are provided in the description text beyond what is already in the schema.

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 function: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question) and output types (evidence packet, comparison), making the purpose unmistakable. While sibling tools like polymarket_edges exist, the unique scope and integration details differentiate it effectively.

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 explicitly lists use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and provides extensive examples covering different bet categories. It also includes blocking conditions (low-confidence match, closed markets). However, it does not explicitly contrast with sibling tools or state when not to use it, leaving some ambiguity for tool selection.

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