ai_tokenize_collection

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

No annotations are provided, so the description bears full responsibility. It mentions the operation is heavy and returns a count, but does not disclose permissions needed, whether data is modified, or if repeated tokenization is idempotent.

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?

Two concise sentences, front-loaded with purpose and behavioral notes. No wasted words.

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 purpose, usage guidance, and return value, but lacks details on parameters and behavioral side effects. For a tool with 3 parameters and no output schema, more context is needed.

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 33% (only 'model' has a description). The description does not explain the 'connection' or 'view_type' parameters, nor does it clarify how they affect behavior. The phrase 'via the registry entry' is ambiguous.

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 tokenizes ALL records of a model, using the registry entry. It distinguishes from siblings like ai_tokenize_record by emphasizing 'ALL' and mentioning alternatives for large models.

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?

It explicitly warns that the operation is heavy and advises using the nightly cron for large models, indicating when not to use it. However, it does not list alternative tools explicitly beyond the nightly cron.

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