Metis · Software Engineer — Ingest Profiling Output

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

No annotations are provided, so the description must bear the full burden. It mentions 'insert/replace' indicating potential overwriting, but does not specify effects on existing data, error handling, or permission requirements.

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 concise and well-structured: a one-line summary, followed by a short workflow explanation, then an Args block. Every sentence adds value without redundancy.

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 the workflow and parameters adequately. Given the existence of an output schema, return values are not required. It could mention prerequisites (e.g., JSON must be from profiling script) but already implies it.

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?

With 0% schema description coverage, the description compensates well by explaining each parameter: json_path, dataset_name, and project_id. It provides usage details like 'absolute path', 'override the dataset name', and 'project to associate'.

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 reads JSON profiling output and registers it as a data dictionary. This distinguishes it from siblings like 'generate_profiling_script' (which produces the JSON) and 'register_data_dictionary' (which might have a different input).

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 explains that the tool is used after running a profiling script locally, which generates a JSON file. It sets the context for when to use it, but does not explicitly list alternatives or when not to use it.

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