slimtoken.list_model_presets

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

With no annotations provided, the description carries the full burden. It discloses that 'List' is a read-only action and explains that measure=true triggers the pipeline to measure live token reduction, implying additional processing. It does not mention auth or rate limits, but for a listing tool this is reasonable context. The description adds valuable behavioral detail beyond the bare schema.

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 consists of two sentences that are tightly packed with relevant information. The main purpose is front-loaded, and the second sentence adds a conditional behavior. There is no fluff or redundancy; every phrase 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 listing tool with two optional parameters and no output schema, the description provides sufficient context: what the tool lists, the filtering criterion, and the optional enrichment behavior. It mentions that each preset 'has a usable context', giving a hint of the return content. It could be slightly more explicit about the exact response structure, but it does not leave a major gap.

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 baseline is 3. The description adds meaning to the 'measure' parameter by explaining it 'enriches each row with the live measured token reduction on a bloated payload (run by the pipeline itself)', which is not in the schema. It also confirms the VRAM tiers (4/8/16GB) that align with the vram_gb parameter, providing useful context.

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 opens with 'List recommended local-model presets by GPU VRAM tier (4/8/16GB)', which is a specific verb+resource+scope statement. It clearly distinguishes this tool from sibling tools like optimize_messages, prune_context, and minify_tool_result, which are mutation/optimization tools, whereas this is a read-only listing operation.

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 clearly implies when to use this tool: when the user needs recommended local-model presets filtered by GPU VRAM tier. It also explains the optional measure=true behavior. However, it does not explicitly contrast with the sibling 'high_context_presets' tool or provide exclusions, so it stops short of full alternative guidance.

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