get_model_details

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implying read-only), but does not address potential errors (e.g., if the model doesn't exist), performance considerations, authentication needs, or rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 clear sections (<instructions> and <parameters>), front-loading the purpose in the first sentence. Every sentence earns its place by specifying the action, resource, returned data, and parameter meaning without any redundant or vague language.

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 the tool's moderate complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It clearly defines the purpose and parameter, but lacks details on error handling, return format (e.g., structure of the output), and behavioral traits like idempotency or side effects, which are important for a retrieval tool with no annotation support.

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. It provides the parameter 'model_name' with a clear semantic explanation ('The name of the dbt model to retrieve details for'), which adds meaningful context beyond the schema's basic type and title. However, it does not specify format constraints (e.g., case sensitivity) or examples, leaving some ambiguity.

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 specific action ('Retrieves information') and resource ('specific dbt model'), distinguishing it from siblings like 'get_all_models' (which lists all models) and 'get_model_parents' (which focuses on dependencies). It explicitly lists the returned information: compiled SQL, description, column names, column descriptions, and column types.

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 implies usage by specifying it retrieves details for 'a specific dbt model', suggesting it should be used when detailed metadata about a single model is needed. However, it does not explicitly state when to use this tool versus alternatives like 'get_all_models' (for overviews) or 'docs' (for documentation), nor does it mention any prerequisites or exclusions.

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