recommend_models

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

With no annotations provided, the description bears the full burden of behavioral disclosure. It does reveal two key traits: 'hard-filter' (some candidates are excluded) and 'deterministically rank' (output order is reproducible). However, it does not mention whether the tool performs network calls (e.g., for live pricing), whether it mutates any state, or if it requires prior actions like scanning an application. These gaps are notable given the 'include_live_pricing' parameter suggests external side effects.

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 one sentence and front-loaded with the core action, which is efficient. However, it is under-specified to the point of being unhelpful. It is not bloated, but it sacrifices substance for brevity, making it inadequate for conveying necessary context.

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?

Given 10 parameters, no schema descriptions, and no annotations, the description is severely incomplete. An agent cannot determine what constitutes a 'compatible' model, how ranking is computed, or what inputs are expected. The presence of an output schema mitigates some return-format concerns, but the input side remains opaque. The description would need to explain parameter semantics and usage context to be minimally 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 0%, so the description must compensate by explaining the 10 parameters. It does not mention a single parameter, their allowed values, or their interplay. An agent cannot infer what 'region', 'platform', 'min_context_window', or 'required_capabilities' mean beyond their names. This is a critical failure for a tool with this many parameters.

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 clearly states a specific action ('Hard-filter and deterministically rank') applied to a resource ('compatible registry models'). It distinguishes itself from siblings like 'compare_models' by focusing on recommendation rather than comparison, and from 'resolve_model' by implying a set-level operation rather than a single lookup.

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?

No guidance is provided on when to use this tool versus alternatives like 'compare_models' or 'generate_migration_plan'. There are no explicit conditions for selection, no exclusions, and no mention of prerequisites (e.g., whether a source model is required). The purpose alone implies a use case, but the description offers no decision support.

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