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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds the LCS algorithm detail and specifies the return format (added/removed/unchanged counts and formatted hunks with configurable context lines), enriching behavioral understanding beyond 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 the core operation, then output details, then usage guidance. Every sentence contributes meaningful information with no redundancy or filler.

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 straightforward 3-parameter tool with full schema coverage and an output schema, the description covers purpose, output, and usage sufficiently. Nothing critical is missing; the LCS algorithm mention and return value summary make it complete.

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 coverage is 100% with descriptive parameter definitions. The description reinforces the context line range but does not add substantial new semantic detail beyond what the schema already provides, matching the baseline for high schema 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?

States a specific verb+resource: 'Compute a unified line-by-line diff between two text strings (LCS algorithm).' This clearly distinguishes it from sibling tools like json_diff, diff_mappings, or levenshtein_distance by focusing on text diffing with line-level granularity.

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?

Gives explicit use cases: 'Use to compare versions of prompts, configs, code snippets, or any text where you need to see exactly what changed.' This provides clear context, though it doesn't explicitly mention alternatives or when not to use, so it stops short of a 5.

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