get-ml-model-service

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

No annotations provided, and description lacks any behavioral details (e.g., whether it is read-only, requires authentication, or has rate limits). The bare phrase 'Get' implies retrieval but adds no transparency beyond that.

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?

Single sentence is concise but too sparse; it lacks necessary detail about parameters or usage context. Front-loads purpose but at the expense of completeness.

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 output schema, no sibling differentiation in description, and parameters are under-documented. The description fails to provide enough context for correct invocation, especially given the large number of similar tools.

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?

Of 3 parameters, only 'fqn' has a description in the schema; 'fields' and 'include' are undocumented. Description does not clarify their purpose or behavior, leaving the agent to guess.

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?

Description clearly states verb 'Get' and resource 'ML model service' with a qualifier 'by name', which is specific and distinguishes from siblings like 'get-ml-model-by-name'.

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?

No guidance on when to use this tool versus alternatives such as 'list-ml-model-services' or 'search-metadata'. Does not specify exclusions or prerequisites.

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