datamodel.get_table_schema

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

Annotations only declare readOnlyHint, openWorldHint, and destructiveHint. The description goes well beyond that by disclosing the POST endpoint, the connection resolution process, the exact return contract (dict or error), and a caution that the endpoint is undocumented and may change. This is substantial behavioral context.

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?

Three sentences deliver purpose+mechanism, return value, and a risk warning with no filler. The key facts are front-loaded and every sentence earns its place.

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 covers what the tool does, how it executes (connection resolution + POST), what it returns on success and failure, and a stability warning. With readOnly and non-destructive annotations covering the safety profile, nothing essential is missing for an agent to invoke it 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?

The input schema already documents all four parameters with descriptions and wire-name mappings, so schema coverage is 100%. The description adds no additional parameter-level meaning beyond the schema's own documentation, making baseline 3 appropriate.

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 opens with 'Retrieve the schema of a table within a connection's data source,' a specific verb+resource statement. It also names the exact API endpoint and scope, distinguishing it from sibling tools like datamodel_get_model_schema, which targets model-level schema.

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 clear context: this is for retrieving table schema from a connection's data source, and it explains the resolution flow via connection_name. However, it does not explicitly state when to prefer this over datamodel_get_model_schema or datamodel_describe_datamodel, nor does it mention exclusions.

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