ag_text_date_normalize

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

With no annotations, the description compensates by disclosing determinism, per-item success flags, batch failure behavior, rate limiting, and provenance envelope. It could be more explicit about exact error conditions, but overall behavior is transparent.

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 information-dense and mostly efficient, but it repeats the payload schema in prose even though the input schema already exists. There is no filler, and the structure is logical.

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 output schema plus the description of parsed/failed counts and per-item flags gives enough context for basic use. It does not detail the shape of each result item, but that is not critical given the output schema.

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 prose explains `dates` and `dayfirst` inside `payload`, but `api_key` is undocumented. Additionally, the input schema allows arbitrary additional properties in `payload`, contradicting the stated payload schema, so parameter coverage is partial and somewhat inconsistent.

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 states a specific operation — normalizing arbitrary date strings to ISO 8601 — with concrete examples, making the tool's purpose immediately clear and distinct from generic JSON/text tools.

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?

No guidance is given on when to choose this tool over sibling tools such as ag_text_regex_extract or ag_json_repair. The description explains behavior but not selection criteria or alternatives.

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