get_model_market

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

With no annotations, the description carries the full load and does disclose the most important behavioral trait: it reads cache only, never refreshes, and performs no network request. It also explains what `intelligence_source` means (Artificial Analysis, user override, or local heuristic), which is genuine value. It stops short of describing ordering, truncation, or what happens when the cache is empty.

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?

Three short sentences, front-loaded with the resource description followed by the two behavioral facts. Some markdown formatting noise (backticks, hard wrapping) but no filler content; every clause carries information.

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?

An output schema exists, so return values need not be explained, and the staleness/network behavior is well covered. The gap is on the input side: for a searchable listing tool with an undocumented `query` and `limit`, an agent lacks enough to call it confidently, and there is no mention of what the cache does not contain.

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 0%, so the two parameters (`limit`, `query`) are undocumented in both schema and description. The description never explains what `query` matches (model name? provider? fuzzy or exact?) or how `limit` interacts with results, leaving the agent to guess at the primary filtering controls.

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?

States a specific verb and resource set: reading cached model prices, context windows, and intelligence scores. It is clear enough to distinguish from siblings like discover_harness_models or preview_routing, though it never explicitly names an alternative or explains how it differs from live model discovery.

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 by the 'cached' framing and the explicit statement that the tool never refreshes and never makes a network request, which tells an agent this is a stale-data read. However, there is no explicit when-to-use/when-not-to-use guidance and no named alternative for getting fresh prices, so the agent must infer the routing decision.

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