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

The description goes well beyond the annotations, disclosing that spreads are gross not net, that Kalshi's fee schedule is not modeled, that unknown legs are never paired, that `temporal_alignment` null means 'could not be computed' rather than aligned, and that `pairing_unverified` is set in both modes. These are exactly the behavioral caveats an agent needs. The `readOnlyHint: true` and `idempotentHint: true` annotations are consistent with the description; no contradiction exists.

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 densely packed with valuable caveats, safety fields, and code semantics. It uses clear labels like TWO MODES, RESPONSE, and SAFETY FIELDS, which aid scanning. It loses a point because the list of pre-mapped shortcuts is duplicated from the schema and the overall length is substantial, though most sentences do earn their place.

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 carries the full burden of explaining the response shape, compatibility codes, fees, skipped-leg counters, temporal alignment semantics, and confidence caveats. It covers essentially everything an agent needs to call this tool correctly and interpret its results, including the important warning that non-empty compatibility warnings can coexist with matched pairs.

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 input schema already documents all three parameters with 100% coverage, so the baseline is 3. The description adds meaningful semantics beyond the schema: it explains the two modes, that explicit parameters override the topic-mapped side, that both modes produce identical disclosures, and that the same token-overlap matcher applies regardless of mode. This is useful but not transformative given the schema already covers parameter names and examples.

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 first sentence clearly identifies the tool's operation: computing the cross-venue spread between Kalshi and Polymarket for the same resolving question. It is specific about the two venues and the metric. However, it does not explicitly differentiate this tool from sibling tools like polymarket_arbitrage or polymarket_edges; the distinction is implied by the venue pair rather than stated.

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 gives explicit mode-level guidance: `topic` shortcuts versus explicit `kalshi_event_ticker` + `polymarket_event_slug` pairings, and notes that both modes run the same matcher. It also warns that pre-mapped topics are not necessarily tradeable and often return compatibility warnings. It does not explicitly say when to prefer this tool over sibling alternatives, so the guidance is strong but not fully comparative.

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