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

Even with annotations already declaring readOnlyHint and idempotentHint, the description goes far beyond them by detailing fan-out behavior, resolver confidence states, low-confidence short-circuiting, closed-market status, illiquidity warnings, and cancellation-rule parsing. This gives agents a thorough map of how the tool behaves and what can go wrong, with no 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 uppercase section labels (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, SAFETY, RESOLUTION-RULE RISK) that make it scannable. Every major section adds necessary operational detail, but it is verbose enough that a more concise version could preserve the same value.

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 is exceptionally complete. It documents the response shape, resolver contract, parent_event extraction, news fallback behavior, blocking conditions, and cancellation-rule implications. This gives an agent everything needed to invoke the tool and interpret results safely.

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 largely restates the schema's parameter semantics (market accepts slug/URL/question text, depth defaults to thorough, include_raw default false) and does not add significant new meaning beyond the schema. Some useful context around fan-out examples exists, but it does not materially enrich parameter understanding.

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 specifies a clear verb and resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It provides concrete query intents ('should I bet on X', 'what does the data say about Y'), which distinguishes it from generic research tools. However, it does not explicitly name sibling tools or contrast itself with alternatives like ask_pipeworx or deep_research, so it stops short of a full 5.

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?

Usage context is explicit: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"' plus numerous domain examples (crypto, Fed, sports, elections). It gives clear when-to-use signals, but it lacks explicit when-not-to-use guidance or named alternatives, so it misses the top score.

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