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

The description adds substantial behavioral context beyond the annotations (readOnly, openWorld, idempotent, non-destructive). It discloses fan-out behavior, GDELT fallback mechanisms, resolution confidence handling, closed-market short-circuiting, wide-spread tradeability flags, and cancellation-rule parsing. It also explains the 'low_confidence_match' and 'market_closed_or_inactive' statuses and how they block further analysis. No contradiction 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 long and dense, but every sentence adds unique value for a complex tool. It is well-structured with clear section labels (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.), and the first sentence front-loads the primary purpose. It could be slightly trimmed, but given the number of behaviors and return-field explanations, the length is 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 high complexity, no output schema, and rich annotations, the description provides an exceptionally complete picture. It explains response shapes, the resolver contract, parent_event extraction, news fallback fields, safety mechanisms, and cancellation-rule risk. An agent would know exactly what to expect from the tool and how to interpret edge cases, making it fully self-sufficient for correct invocation and use.

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 baseline is 3. The description repeats the market parameter's accepted formats (slug, URL, question text) but does not add new syntax details beyond the schema. It does provide examples of fan-out behavior that indirectly clarify depth, but it doesn't explicitly connect depth to those examples. The include_raw parameter is not mentioned in the description, though the schema covers it. Overall, the description doesn't significantly enhance parameter understanding beyond what the schema already provides.

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 core function: researches a Polymarket bet by pulling relevant Pipeworx data in one call. It specifies the resource (a Polymarket bet), the action (research/pull data), and the method (resolves, classifies, fans out). It also distinguishes from siblings by focusing on a specific bet's evidence packet and market-vs-model comparison, unlike broader tools like deep_research or 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 tells when to use it: for questions like 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'. It provides abundant examples of fan-out scenarios. However, it does not explicitly mention alternatives or when not to use it, though the use cases are sufficiently clear. Safety warnings about low-confidence matches and closed markets provide cautionary context.

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