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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description goes far beyond with operational details: resolver contract (market_match_confidence, alternatives, suggestions), safety short-circuits (status:'low_confidence_match' for low-confidence matches, status:'market_closed_or_inactive' for dead markets), wide-spread illiquidity warnings, and cancellation-rule risk (refund_50_50 with EV impact). This is exceptional behavioral disclosure.

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 exceptionally well-structured with clear section headings (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, RESOLVER CONTRACT, etc.). Every section adds operational value. It is not 'concise' in length but is efficiently organized and front-loaded with the core purpose. A 5 would require tighter prose; a 4 reflects good structure despite length.

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 (classifiers, many data sources, resolver logic, response shapes, safety behavior) and the absence of an output schema, the description is remarkably complete. It covers response shapes (result.market, result.analysis, result.evidence, result.parent_event), news fields, blocking behaviors, and cancellation rules. No critical context is missing.

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%—all three params (market, depth, include_raw) have detailed descriptions. The tool description adds real-world input examples (slug, URL, question text) and explains how the resolver handles ambiguous matches, which nuances the market parameter. Given the high schema coverage, a baseline of 3 applies, but the description's examples and resolver context push it to a 4.

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+scope: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly states what the tool does (resolves, classifies, fans out, returns evidence packet + market-vs-model comparison) and distinguishes it from general research tools like ask_pipeworx and edge-specific tools like polymarket_edges.

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: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides concrete fan-out examples across bet categories. However, it does not explicitly state when NOT to use the tool or mention alternative sibling tools, so it falls short of a 5.

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