models_summary

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

With no annotations, the description carries the full burden and does well by detailing exactly what compact=True emits (name, description, column count, measure names, joins list) and what it omits. It also discloses the default behavior and the compact=False alternative. It doesn't discuss errors or side effects, but none are expected for a read-only summary tool.

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 well-structured with a clear opening sentence followed by rendering details and an Args list. The DEV-1549 ticket reference is noise for an AI agent, but all other sentences earn their place by clarifying output behavior and parameter choices.

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?

The description fully covers the parameters and output rendering modes, and an output schema exists to define the structured return shape. It could improve by naming sibling alternatives or noting datasource-not-found behavior, but for a summary tool with this complexity it is largely complete.

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 it does. Each parameter is explained in the Args section: datasource_name references list_datasources, format names valid values and case-insensitivity, and compact explains default and behavioral impact. This adds substantial meaning beyond the bare input 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 identifies the tool's purpose: 'Brief summary of all (non-hidden) models in a datasource.' It specifies the resource scope (all non-hidden models) and gives a distinct action, though it doesn't explicitly contrast with siblings like inspect_model or describe_datasource.

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 gives clear context for when the tool would be useful—summarizing all models in a datasource—but it never explicitly states when to prefer this over siblings such as inspect_model or describe_datasource. It does explain usage for compact versus verbose output and format selection, but tool-alternative guidance is implied rather than stated.

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