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

With no annotations, the description carries the full behavioral burden. It discloses that the tool is read-only, explains REAL vs MODELAT data provenance, describes unknown-card error behavior, and states that ambiguous cards are not auto-resolved. This is substantial transparency beyond the 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 is front-loaded with purpose, then efficiently covers differentiator, data provenance, read-only safety, and error/ambiguity behavior. It is somewhat long, but every sentence adds distinct information and there is no filler.

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 no annotations and no output schema, the description covers the core purpose, speed estimates, data provenance, read-only safety, unknown-card errors, and ambiguous-card handling. It does not spell out the exact output structure or optional parameter effects, but those are partially covered by the schema.

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 useful context by linking gpu and ram_gb to the core question and explaining gpu ambiguity handling, but it does not add syntax or format details beyond what the schema already provides for quant and ram_type.

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 and resource: give it a graphics card and system RAM, and it answers which language models will run and roughly how fast. It also distinguishes itself from ordinary VRAM calculators by accounting for mixture-of-experts models, making its purpose and differentiator clear.

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 gives a clear use case and explicitly contrasts itself with ordinary VRAM calculators, which serves as an exclusion for a broad alternative category. However, it does not name sibling tools like try_a_model or state explicit when-not-to-use conditions, so the guidance is strong but not fully explicit.

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