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

With no annotations, the description carries the full behavioral burden, and it does disclose meaningful traits: in-process (not persistent) caching, cache-hit responses marked cached=true, and that persistent per-account caching is a paid feature. It does not say whether the call has side effects, what auth it needs, or how cache scope/eviction works.

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 with the core differentiator (cache-first) front-loaded and no filler. The middle clause about demonstrating the caching layer is marginally self-referential but still informative.

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?

For a six-parameter tool with no annotations and no output schema, the description covers the caching behavior and one return field (cached=true) but omits any parameter meaning and any note on the interaction between include_paid and the paid persistent cache. Adequate on behavior, thin on inputs.

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% across six parameters (prompt, region, priority, include_paid, max_output_tokens, required_capabilities), and the description explains none of them. Only 'identical requests' loosely hints that prompt is the cache key, leaving the remaining parameters undocumented in both places.

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 states the tool returns a routing plan and frames it relative to the sibling 'route' ('Same as route, but checks an in-process cache first'), which lets an agent distinguish the two. The gap is that it assumes the agent already knows what 'route' does rather than restating the core verb+resource.

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?

'Repeated identical requests return the cached plan' implies when this tool pays off versus plain route, but there is no explicit when-to-use/when-not statement or named alternative. Usage is inferable rather than stated.

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

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

With no annotations, the description carries the full burden, yet it usefully discloses that costs are estimates (not live/actual billing), that the result is a report layer, and that it covers all 'reachable' models. It omits whether the call is side-effect-free, whether it hits a live pricing API, or any rate/auth constraints.

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?

Two tight sentences with the core action front-loaded. The parenthetical '(the monetization "report layer")' is internal jargon that does little work for an agent, a minor deduction.

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 correctly sketches the return (cost report including max savings vs the most expensive reachable model). However, half its parameters are undocumented anywhere, leaving an agent guessing about region and capability filtering.

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 only 25% — only 'prompt' is documented, while region (with its global/CN enum), max_output_tokens, and required_capabilities are bare. The description only echoes 'prompt size' and adds no semantics for the three undocumented parameters, so it fails to compensate for the coverage gap.

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 ('Rank every reachable model by estimated cost') plus the output artifact (cost-comparison report with max savings). This clearly distinguishes it from the sibling list_models (enumerate only) and cache_route/route (actual routing), though it never names those alternatives.

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 implies a pre-routing comparison use case but never states when to call this versus route, cache_route, or list_models, nor any exclusions or prerequisites. No explicit 'use this when...' guidance is present.

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

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

No annotations are provided, so the description carries the behavioral burden. 'List' implies a safe read, and 'curated' hints that results are a maintained subset rather than a raw feed, which is a small useful signal. However, it says nothing about result count, ordering, pagination, or whether any filtering requires auth, leaving real behavioral gaps for an unannotated tool.

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?

A single sentence with zero filler, and the primary action plus the filtering capability are front-loaded. Every clause earns its 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?

For a simple three-optional-parameter listing tool with no output schema, the description covers purpose and filters adequately. It omits what the listing contains (fields returned) and any ordering or pagination behavior, which a caller in a routing workflow would plausibly want to know, so it is adequate but not complete.

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% and each of the three filters is documented in the schema with enums and defaults, so the schema does the heavy lifting. The description names the same three filters, adding no syntax, format, or combination semantics beyond what the schema already states; 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?

States a specific verb and resource ('List the curated model registry') and enumerates the three filter axes, so the agent knows exactly what the tool returns and how it can be narrowed. It does not explicitly differentiate itself from the routing siblings, but those names (route, cache_route, cost_compare) are operationally distinct enough that confusion is unlikely.

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 phrase 'optional filters' implies the tool works with or without arguments, which is useful, but there is no explicit when-to-use guidance or mention of alternatives (e.g. routing through a model vs. enumerating candidates). Usage is implied rather than stated.

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

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

No annotations are provided, so the description carries the full burden. It adds real value by disclosing the return payload (chosen model, USD+CNY cost estimate, fallback chain, reasoning) and the underlying channel pool, but it never resolves whether the tool actually dispatches the prompt or only returns a routing recommendation, and says nothing about auth or rate limits.

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?

Two sentences, front-loaded with the action and followed by the payoff and source pool. The trailing channel list is slightly decorative but does convey scope; nothing is bloated.

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 six parameters, no output schema, and no annotations, the description does the important extra work of naming the return fields and cost units. The remaining gap is the execute-vs-recommend ambiguity, which an agent needs in order to use the result correctly.

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% and the schema already explains region, priority, include_paid, max_output_tokens, and required_capabilities with defaults and enums. The description adds no parameter-level detail beyond 'prompt', so the baseline 3 applies.

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: 'route [a prompt] to the cheapest reachable free/cheap LLM', and enumerates what comes back (model, cost, fallback chain, reasoning). This is clearly distinguishable from cost_compare and list_models on intent, though it never names a sibling to sharpen the boundary.

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 context is implied rather than stated: an agent can infer 'use me when you want a cheap model selected for a prompt', and the sample model channels suggest scope. There is no explicit when-not condition and no mention of cache_route or cost_compare as alternatives for similar needs.

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