df_check_import_source

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

Annotations already indicate idempotent and non-destructive behavior. The description adds value by clarifying that it is a dry run that writes nothing, and by disclosing the exact output shape (valid, errors[], warnings[], element counts). It also implies network/credential usage implicitly through the authentication parameter, but that is not explicit. It does not contradict annotations; readOnlyHint false is consistent with it being a validation that may read external sources without writing. The extra context on return fields goes beyond what annotations provide.

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 a single, efficient sentence that front-loads the most critical information ('Dry run') and immediately communicates the key behavior (validate, no writes) and output. There is zero redundancy; every word earns its place. It is concise without sacrificing clarity.

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 complexity (11 parameters, nested authentication object, two source types), the description is far too brief. It does not explain how to specify the source (git vs file), which parameters are required for each source_type, how authentication is configured, or what 'element counts' refers to. There is no output schema, so the description's brief return list is helpful but insufficient for an agent to correctly assemble the many optional parameters. The tool clearly needs more operational context to be used 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 only 36%, with 7 of 11 parameters lacking any description. The tool description does not explain any parameter semantics, such as how to specify git vs file source, which parameters are required for each source_type, or how authentication is used. The description only mentions 'import source' generically without mapping to the schema. With low schema coverage, the description should compensate but does not, leaving the agent without sufficient guidance to fill in the many undocumented parameters.

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's purpose: it is a dry-run validation of an import source, explicitly saying it writes nothing. It names the resource ('import source') and the action ('validate'), and the return structure (valid, errors, warnings, element counts) distinguishes it from import tools like df_import_version_from_git and preview tools like df_preview_import. The verb is specific and the scope is unambiguous.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention that it is a pre-flight check before an actual import, nor does it contrast with df_preview_import or the import tools. The description implies a validation role, but it never explicitly says 'use this before importing' or names alternative conditions. An agent has to infer usage from the tool name and siblings.

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