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

The description goes far beyond the annotations, disclosing detailed internal logic: three model families with formulas, placeholder-slug filters, Kelly caps (0.25), slippage handling, 24h-move warnings, diagnostics structure, and a limitation note (Fed signal unreliable). It also explains why segments can be empty and how filtering gates work. This is exemplary behavioral 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 dense and long, but every sentence carries substantive information. It is organized with labeled segments and parenthetical details, but the wall-of-text format could benefit from more visual structure (e.g., headers, bullet points). Given the tool's complexity, the length is justified, though slightly more concision would improve readability.

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 fully compensates by detailing the top-level response structure (by_segment, fed_candidates, _diagnostics), explaining empty-segment diagnostics, and covering caching behavior. It also documents all major behavioral aspects, including data sources and limitations, making the tool's context complete for an agent.

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?

The schema already describes all 9 parameters (100% coverage), so the baseline is 3. The description adds significant semantic value by defining key concepts like edge_pp_net, explaining that min_kelly does not apply to partition_overround by design, and clarifying the distinct role of min_partition_leg_kelly. It also provides example thresholds for min_liquidity and max_spread_pp, enhancing understanding beyond the schema.

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 is exceptionally clear: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It further specifies the intended use case ('what should I bet on today') and differentiates from siblings by emphasizing its discovery function without paging hundreds of markets, unlike arbitrage or risk-specific tools.

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 provides strong contextual guidance: it states the tool is 'Built for "what should I bet on today"' and explains when it is appropriate (e.g., discovering opportunities, with caching noted). It also mentions an exclusion (Fed bets surface but are excluded from ranking) and offers knob adjustments. However, it does not explicitly name alternative tools for different use cases or say 'use X instead', so it lacks explicit exclusions/alternatives.

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