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

No annotations are present, so the description must carry the full burden, and it does: it discloses position-based matching, the lack of LCS behavior, the absence of patch output, whitespace/case significance, CRLF/LF normalization, and local execution cost. This is exemplary transparency beyond the structured 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?

Four dense sentences with no filler: return shape first, then algorithm behavior, then limitations, then normalization/execution details. Every sentence contributes essential information for correct invocation.

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?

Despite having no annotations and no output schema, the description fully specifies the return contract, algorithm semantics, edge-case handling, and cost characteristics. An agent has everything needed to select and call the tool 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% and the property descriptions already map text1 to baseline/before and text2 to revised/after. The main description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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?

Begins with a specific verb and resource ('Compare two blocks of text line by line') and immediately states the exact JSON return shape. The contrast with generation-oriented siblings like generate_text and draft_email is clear without ambiguity.

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?

Clearly states the tool is position-based and not an LCS diff, and explicitly rules out character-level, word-level, and unified-diff use cases. It does not name a sibling alternative, but none of the sibling tools are diff tools, so the guidance is sufficient.

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