diff-patch-tools

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return value ('return the patched result') but does not disclose what happens on invalid patches, whether the patch is applied exactly or with fuzzing, error handling, or any side effects. This lack of detail is a significant gap for a tool that operates on potentially malformed input.

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 a single sentence that is direct and front-loaded. It states the action ('Apply a unified diff patch') and the result ('return the patched result') with no unnecessary words. Every word 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?

For a simple two-parameter tool, the description covers the core operation and output. However, it omits important context such as error behavior, patch format assumptions (beyond 'unified diff'), and whether the operation is destructive or reversible. Given no annotations and no output schema, this leaves the agent without critical edge-case knowledge.

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 input schema already provides descriptions for both parameters ('original' and 'patch'), covering 100% of the schema. The description adds no additional semantic meaning beyond what the schema states, so the baseline 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?

The description uses a specific verb+resource structure: 'Apply a unified diff patch to an original text and return the patched result.' This clearly states what the tool does and differentiates it from sibling tools like diff_stats or patch_validate, which compute diffs or validate rather than apply patches.

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 implies the use case (applying a patch) but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or when to prefer siblings like patch_validate or three_way_merge. The usage context is clear by inference, but not explicitly stated.

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

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

With no annotations, the description carries the burden. It discloses the core behavior (parsing and returning stats) but does not mention edge cases (e.g., malformed diffs, handling of binary files) or output structure details beyond the statistic names.

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 one sentence, front-loaded with the action and resource, and contains no filler. Every part contributes to understanding.

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 single-parameter tool with no output schema, the description covers the input, action, and the key output statistics. It is complete enough for an agent to know what to expect, though it could specify the exact return structure or data types.

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% for the 'diff' parameter. The description only repeats 'unified diff string' without adding extra syntax, format expectations, or examples 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 clearly states the verb 'parse' and the resource 'unified diff string', and lists the specific statistics returned (additions, deletions, hunks, files changed). This distinguishes it from sibling tools like text_diff or apply_patch.

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 implies its usage (when you need line-level diff statistics) but does not explicitly state when to use it over alternatives or when not to use it. No exclusions or alternative tool references are provided.

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

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

With no annotations provided, the description must carry the burden of behavioral disclosure. It mentions output categories but fails to describe the exact return format (e.g., object shape, list of changes) or edge-case behavior. It also doesn't explicitly state that the operation is non-mutating, though that is implied by the diff nature.

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 a single sentence of 15 words, front-loaded with the action and resource, and contains no redundant or filler content. Every word 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?

The tool is simple with two well-described required parameters and no output schema. The description conveys purpose and output categories but lacks explicit return structure or limitations. Given the sibling context, an agent can infer usage, but completeness is only moderate.

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?

Input schema provides 100% coverage with clear descriptions for both parameters ('Original JSON value (object, array, or JSON string)' and 'Modified...'). The tool description adds only the relational context between them, which is minimal; 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 uses a specific verb 'compute' and a clear resource 'structural diff between two JSON values', explicitly listing output categories (added, removed, replaced). This distinguishes it from sibling tools like text_diff, which handles text comparison.

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 implies use for structural JSON comparison but does not explicitly state when to use this tool over alternatives like text_diff or diff_stats. No exclusions or alternative tool names are mentioned, leaving usage context mostly implicit.

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

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

With no annotations, the description carries the transparency burden. It clearly discloses the key behavioral trait: this operation does not apply the patch, making it non-mutating. It stops short of detailing return values or error behavior, but the core side-effect-safe nature is explicit.

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?

A single, front-focused sentence that states the purpose and primary constraint ('without actually applying it'). There is no wasted text or redundancy.

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 tool is simple, but without an output schema the description should clarify return semantics. 'Check whether' implies a boolean result, and the non-mutating nature is stated. Missing details like how malformed patches are handled, but for a validation tool this is adequate.

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% (both parameters described in the schema), so the description adds no new parameter semantics beyond the existing field descriptions. The baseline of 3 applies because the schema fully documents the parameters.

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 uses a specific verb ('check') and identifies the resource ('unified diff patch' and 'original text') with a clear scope: validate applicability without applying. This distinguishes it from siblings like apply_patch and diff_stats by emphasizing the non-mutating validation intent.

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 'without actually applying it' implies a safe validation use case, contrasting with apply_patch. However, it does not explicitly name alternatives or provide exclusions, leaving the when-not-to-use scenario implicit.

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

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.

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

With no annotations, the description carries the burden of behavioral disclosure. It provides one useful behavioral detail (conflicts marked with diff3 markers) but omits other important traits such as whether the operation is read-only, the exact return format, or how it handles edge cases like identical inputs. This is a partial disclosure.

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 a single, front-loaded sentence that is direct and free of redundancy. Every word contributes to understanding the tool's purpose and key behavior, making it highly concise without losing substance.

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 tool is simple, and the schema fully documents all parameters. However, with no output schema, the description should explicitly indicate what the tool returns (e.g., merged text), which it does not do. The mention of conflict markers implicitly suggests the output, but the lack of explicit return information leaves a completeness gap.

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%, so the baseline is 3. The description names the required parameters (base, ours, theirs) and mentions labels only indirectly via conflict markers, but it does not add meaning beyond the schema's parameter descriptions. No extra semantic value is provided.

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 function as a three-way merge with specific resources (base, ours, theirs) and includes a behavioral detail about conflict markers. It distinguishes itself from sibling tools like text_diff and apply_patch by focusing on merging rather than diffing or patching.

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 implies the usage context (when you have a base and two modified versions) but does not explicitly state when to use this tool versus alternatives like text_diff or apply_patch. No exclusions or alternative recommendations are provided.

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