recommend_model

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

With no annotations, the description carries the full burden and does well: it discloses the selection heuristic (prefers tool-calling models fitting free VRAM, or free RAM for CPU-only) and the write side effect of apply=true ('writes the pick... AND applies it live'). It stops short of stating failure behavior when nothing fits or permission requirements.

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 tightly packed sentences: the core recommendation purpose leads, the selection heuristic follows, and the mutating apply behavior closes. Every clause carries information and nothing is redundant.

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 tool with full schema coverage and no output schema, the description supplies the missing behavioral context (selection criteria and the apply side effect). It's nearly complete; only failure/edge-case behavior when no model fits the budget is unaddressed.

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 the schema already documents all four parameters, which sets a baseline of 3. The description adds some behavioral meaning for apply (config write + live application), but adds nothing beyond the schema for model, provider, or budget_mb.

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 a specific verb ('recommend a default_model') and resource (a provider's default model) and pins down the unique logic: analyzing installed models against detected hardware. This clearly separates it from siblings like list_models or get_system_resources, which only surface raw data rather than producing a recommendation.

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?

It implies usage through the apply=true clause (persist/live-apply) but never states when to prefer this tool over list_models or get_system_resources, nor any prerequisites or exclusions. The intended workflow is inferable but not spelled out.

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