strip_evaluateandlog

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

No annotations are provided, so the description carries the full burden. It discloses the scope of the operation (targeted vs whole model) but does not state whether the operation is destructive, reversible, or requires any permissions. For a mutation tool, this leaves significant behavioral transparency gaps.

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 two sentences, front-loaded with the action, and contains no redundant information. Every clause adds value.

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 explains the core modes of operation but lacks information on edge cases (e.g., passing only table or only measure), return values, or safety considerations. With no output schema and no annotations, a bit more detail on effects and edge cases would be needed for full completeness.

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 67%, and the description adds crucial semantics by explaining that table and measure must be used together or both omitted. This pairing logic is not present in the schema and directly compensates for the partial coverage.

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 uses a specific verb 'Remove' with a clear object 'EVALUATEANDLOG wrappers' and states the two modes of operation (targeted measure or whole model). It distinguishes itself from the sibling tool inject_evaluateandlog by clearly being the inverse operation.

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 explicit usage context: 'clean-up after debugging' and explains when to pass table+measure vs omit both. It does not explicitly name alternatives, but the usage context is clear and actionable.

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