estimate_model_fit

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

With no annotations, the description carries the full load and does reasonably well: it discloses that results are a 'rough estimate', enumerates what the estimate includes, and warns that falling back to default GQA assumptions will be flagged in the result. It omits any statement about accuracy bounds, error behavior for unknown gpu_id/architecture strings, or whether quantization is honored in the weights term.

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?

Four short sentences, front-loaded with the purpose before the method and the parameter caveat. No filler or repetition; the only inefficiency is that the second sentence's component list is more about output composition than about how to call the tool.

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?

No annotations, no output schema, and six parameters at zero schema coverage; the description explains one of them. For a tool whose answer swings on quantization and context_length, leaving those semantics undefined means an agent cannot reliably construct a correct call or interpret the 'flagged' default.

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 and largely does not: only 'architecture' is explained. The semantics of gpu_id, model_size_b, quantization (default 'q4'), context_length (default 8192) and parallel_requests (default 1) are left entirely to bare, undescribed schema fields, even though context_length and parallel_requests materially change the KV cache result.

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: 'Estimate whether an LLM fits in one GPU's VRAM', and even names the components of the estimate (weights + runtime overhead + KV cache). It is clearly distinct from metric-reading siblings, though it never explicitly contrasts with recommend_gpu_for_model, which is the nearest neighbor.

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?

Guidance is limited to one conditional: pass 'architecture' for an architecture-aware KV cache, otherwise defaults apply and are flagged. There is no statement of when to prefer this over recommend_gpu_for_model or find_available_gpu, and no prerequisites (e.g. does gpu_id need to be valid/existing?).

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