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

Even though annotations already mark the tool as read-only, idempotent, and non-destructive, the description goes far beyond by disclosing resolver contracts (match confidence, alternatives), short-circuit paths for low-confidence/closed markets, illiquid spread warnings, cancellation-rule parsing, and news fallback mechanics. This level of operational detail is exemplary and gives agents strong behavioral expectations.

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 every sentence carries actionable information. It uses clear all-caps section headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) to structure complex content. Each section addresses a distinct aspect (filters, output, safety, edge cases) without redundancy, making the density justified.

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 complexity and the absence of an output schema, the description thoroughly explains return shapes (market, analysis, evidence), resolver contract (match confidence, alternatives, suggestions), parent_event extractor, news fallback fields, blocking statuses, and cancellation-rule risk. It leaves no major ambiguity for an agent deciding whether to trust the 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 description coverage is 100%, so the schema already fully documents the three parameters (market, depth, include_raw). The description adds real-world examples of market input formats and explains the tradeoffs of include_raw (response size) but does not introduce meaning beyond what the schema provides. Therefore, baseline 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 specific verb+resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It also distinguishes the tool from siblings like polymarket_edges and ask_pipeworx by focusing on bet-resolution data fan-out. The classifier list and fan-out examples further clarify the unique scope.

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 states when to use it: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also gives concrete fan-out examples by category. However, it does not explicitly name alternative tools to use instead, nor does it state when not to use this tool, so it stops short of full exclusion guidance.

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