_mcp_quantize_model

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions dry-run behavior and the conditional execution, but it does not describe side effects (e.g., file creation, model changes), required permissions, or error conditions. Significant gaps remain.

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 very concise (single sentence) and front-loaded with the purpose. However, its brevity sacrifices important details, such as parameter explanations and output description. It is adequately concise but incomplete.

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 five parameters and a complex domain (model quantization), the description is insufficiently complete. It does not explain parameter roles, expected input format, or return value (despite an output schema existing). The agent would likely need additional information to use the tool correctly.

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%, meaning no parameters are documented in the schema. The description adds no information about any of the five parameters (bits, format, model_path, output_path, local_python). Thus, it fails to add meaning beyond the schema.

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 the tool emits a quantization command for specific formats (GGUF/GPTQ/AWQ), using a specific verb ('emit') and resource ('quantization command'). It is distinct from all sibling tools, none of which deal with quantization.

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 provides a condition for when the tool actually runs versus dry-run ('unless FTOS_LOCAL_PYTHON configured'), offering some usage guidance. However, it does not explicitly state when to use this tool over alternatives, and no alternatives are mentioned, so guidance is limited.

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