Generate a unified diff between two text strings. Ideal for comparing file versions before and after edits.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Context lines around each change (default 3) | |
| modified | Yes | Modified text | |
| original | Yes | Original text | |
| modified_file | No | Label for the modified file (default "modified") | |
| original_file | No | Label for the original file (default "original") |
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 core behavior (generating a unified diff) but doesn't mention potential limitations such as handling of large inputs or exact output formatting. The operation is a pure function, so no side effects are expected, but this is not explicitly stated.
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, with the primary action front-loaded: 'Generate a unified diff between two text strings.' The second sentence adds useful usage context without redundancy or wasted words.
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 gives the essential purpose and a use case. It doesn't explicitly state the return type, but that's implied by 'generate a unified diff.' Given the simple nature of the tool and full schema coverage, the description is adequate, though not exhaustive.
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?
The schema covers all 5 parameters with descriptions, so the description adds no additional parameter semantics. The mention of 'unified diff' aligns with the context parameter (context lines) but doesn't elaborate beyond the schema, so baseline 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?
The description clearly states the tool's action and resource: 'Generate a unified diff between two text strings.' It also distinguishes itself from sibling json_diff by explicitly focusing on text strings rather than JSON.
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 a concrete use case: 'Ideal for comparing file versions before and after edits.' It does not explicitly exclude alternatives like json_diff or diff_stats, but it gives clear context for when text_diff is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: applying patches, validating patches, generating diffs for text and JSON, computing stats, and merging with conflict markers. No two tools could be confused with each other based on their descriptions.
Most tool names follow a similar object_verb pattern (json_diff, text_diff, patch_validate, three_way_merge), but apply_patch breaks this with verb_object order, and diff_stats is an odd noun-noun combination. Overall still readable and largely consistent.
Six tools is an ideal size for a focused diff/patch toolkit. Each tool serves a distinct function without redundancy or unnecessary bloat.
The toolkit covers the full lifecycle of diffing, patching, validating, analyzing, and merging, with specific support for both text and JSON. No obvious gaps in the stated domain.