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

Annotations already assert readOnly, idempotent, non-destructive. The description adds meaningful behavioral context: computation is deterministic (LCS) and not based on model guessing, which affects trust in output. It also clarifies output includes line numbers, complementing annotations.

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 sentences, front-loaded with core behavior and algorithm, then usage context. No wasted words; every sentence earns its place.

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 simplicity, the description covers purpose, usage, and algorithmic behavior. An output schema exists (not shown) to handle return details, so the description does not need to explain output structure. It is complete for an agent to select and invoke 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 100% with each parameter described as 'first (before) text' and 'second (after) text.' The description does not add further parameter-level detail, so baseline 3 is appropriate; the schema fully handles parameter semantics.

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 verb ('Returns') and resource ('which lines were added and which were removed, with line numbers'), clearly distinguishing it from siblings like diff_tables. It also clarifies it uses longest-common-subsequence, not a model, reinforcing its specific function.

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?

Provides explicit use cases: 'compare two versions of a config, a document, or any command output' and explicitly contrasts with 'asking an LLM to eyeball two blobs.' This gives clear guidance on when to choose this tool over an alternative approach.

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