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

With no annotations, the description discloses useful behavior: it is deterministic and does not call an LLM, it is backed by a maintained model+price catalog, and it returns a fallback chain plus an explicit do-not-use list with reasons. It would be stronger if it stated whether the call has any side effects or external dependencies beyond the catalog.

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?

Front-loaded and short, but written in terse fragments ("A maintained model+price catalog.") and the closing sentence ("Tells the AI/user which model to use and what it costs") largely restates the opening, costing efficiency.

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 7-parameter tool with no output schema and no annotations, the description adequately conveys the return shape (primary+fallbacks, do-not-use with reasons, complexity tier, per-run and monthly cost). Gaps remain around the undocumented numeric parameters and any runtime constraints.

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 coverage is only 29% (only top_n and models are documented), so the description must compensate, and it does: it explains volume/tokens inputs and gives concrete examples for the undescribed constraints array (local-only, cheap). It still doesn't clarify tokens_in/tokens_out defaults or volume_per_month units.

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?

Opens with a specific verb+resource: an LLM selection and cost advisor that takes a task description and returns a recommended model, fallback chain, and cost. It is clearly distinguishable from siblings like tool_advisor or buy_credits, which concern tool choice and purchasing rather than model selection.

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 usage ("POST a task description... get a recommended model") but never states when an agent should call this versus another tool, nor any exclusions. No alternatives are named and no prerequisites are given.

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