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

Annotations indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds extensive behavioral context beyond these: market resolution confidence levels (match confidence, score, alternatives), parent event extraction, news fields with fallback/retry logic, safety short-circuits for low-confidence matches, closed markets, wide spreads, and resolution-rule risk parsing. No contradictions with annotations.

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 very long and dense, covering many details. It is well-organized with clear sections (classifiers, fan-out examples, response shapes, resolver contract, etc.) and front-loaded with the core purpose. However, the length might overwhelm an agent; some details could be streamlined or split into a separate guide, but the structure is logical and every sentence adds 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, three input parameters, and no output schema, the description is remarkably complete. It covers input formats, behavior for different bet types, response structure (market, analysis, evidence), error states (low confidence, closed markets, wide spreads), safety warnings, and resolution-rule risks. All essential information for correct agent invocation is present.

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% with all three parameters described. The description adds significant meaning: the 'market' parameter can be a slug, URL, or question text; 'depth' options are clarified as quick vs thorough with source counts; 'include_raw' is explained with size implications (summarized vs full payloads) and use cases. This goes well beyond schema definitions.

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 purpose: research a Polymarket bet by pulling Pipeworx data, resolving the market, classifying, fanning out to data packs, and returning evidence with comparison. It uses specific verbs ('Research', 'pull', 'resolve', 'classify', 'fan out', 'return') and is easily distinguished from sibling tools like polymarket_arbitrage or polymarket_edges, which have different objectives.

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 the tool: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. It provides multiple examples and outlines behavior for specific scenarios. While it doesn't explicitly state when not to use it, the context makes it clear, and sibling tools cover alternative use cases.

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