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

Annotations already declare read-only, idempotent, open-world, and non-destructive. The description massively adds behavioral context beyond that: resolver contract (market_match_confidence, score, alternatives), safety short-circuits (low_confidence_match, market_closed_or_inactive), illiquid_wade_spread flag, resolution-rule risk (cancellation_rule with examples like refund_50_50), news fallback behavior (fallback_attempted, retry_after_sec), and even a warning about model edge being priced in. This is exemplary transparency.

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 is purposeful and packed with relevant behavior. It is front-loaded with the core purpose in the first line, then uses CAPITALIZED section headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) to make information navigable. There is no filler; the complexity of the tool justifies the 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?

With no output schema, the description must fully explain what the tool returns. It does this thoroughly: result.market fields, result.analysis with model probability and edge warning, result.evidence keying, resolver contract with match confidence and alternatives, parent_event extractor for elections/championships, news fallback fields, and safety statuses. It even covers edge cases like low-confidence, closed markets, and wide spreads. This is complete enough for an agent to confidently invoke the tool.

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% and all three parameters have clear descriptions in the input schema. The tool description reinforces the market parameter formats (slug, URL, question text) and adds context about depth and include_raw via the schema itself, but beyond that it does not add new parameter-level semantics. Baseline 3 applies because the schema already does the heavy lifting.

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 and resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly distinguishes itself from sibling tools like polymarket_edges or plymarket_arbitrage by focusing on bet research and evidence gathering, not just edge detection or arb. The scope is explicit: accepts slug/URL/question text and returns a structured evidence packet.

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 the intended use: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' This is strong when-to-use guidance. However, it does not explicitly mention when NOT to use this tool or name alternative sibling tools for different purposes, so it misses the 'when-not/alternatives' part that would earn a 5.

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