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

With no annotations, the description carries the burden and discloses key behavioral traits: LCS-based determinism, unified-diff output, and a per-call price. It does not describe limits, error behavior, or auth mechanics beyond the price, but it adds meaningful context beyond the schema.

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: the core operation comes first, followed by a compact differentiator and pricing note. No sentence feels redundant or bloated.

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?

For a simple read-only diff tool with complete parameter schema coverage and no output schema, the description sufficiently explains what the tool returns (unified-diff additions, removals, context). It omits input size limits or error cases, which are minor gaps.

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 documented in the input schema. The description mentions line-by-line comparison and context but adds no parameter syntax or constraints beyond what the schema already provides.

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 and resource: compare two texts line by line. It also names the algorithm (LCS diff) and output format (unified-diff style), so an agent can distinguish this from generic text tools without opening the schema.

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 phrase 'Deterministic, unlike asking a model to find the differences' implies a usage context, but it does not explicitly say when to choose this tool over alternatives or when not to use it. Usage remains largely inferred from the tool's name and purpose.

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