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

With no annotations, the description carries the full burden and does so well: it discloses data provenance and staleness (background snapshot rebuilt every 30 minutes), billing behavior ($0.02 per call from shop credits, failed/empty calls free), the auth path (get a key via buy_credits or /buy/credits), the presence of a match_confidence signal, and an explicit facts-only disclaimer. These are exactly the traits structured fields cannot convey.

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?

It is on the long side, but front-loaded with the resource and its output fields, then layered with the details an agent needs (freshness, cost, disclaimer). Nearly every sentence carries information, though the list of returned fields borders on over-enumeration.

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?

There is no output schema, so the description compensates by describing the return fields, and with no annotations it supplies the safety/cost/auth context an agent needs before calling. Nothing material for correct invocation is missing.

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 q, limit, and min_gap are already documented in the schema; the description adds no syntax, units, or edge-case semantics beyond it. The unit hint ('gap in points') appears in the return-value discussion rather than for the parameters, so baseline 3 is appropriate.

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 names a specific operation and resource — the biggest cross-platform disagreements between Polymarket and Kalshi on the same question — and enumerates the exact payload (both questions, probabilities, gap, volumes, close dates, urls, match_confidence). This scope is inherently distinct from single-platform siblings like pm_odds or pm_search, so an agent can select it unambiguously.

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?

Usage is implied (find the largest cross-platform mispricings) and the freshness note (snapshot rebuilt every 30 minutes) plus the 'check both questions resolve on the same terms' caveat give useful operating context. However, it never explicitly states when to prefer this over pm_odds or pm_search, nor any exclusion conditions, so routing is left to inference.

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