Compute a structural diff between two JSON values, showing added, removed, and replaced fields.
| Name | Required | Description | Default |
|---|---|---|---|
| modified | Yes | Modified JSON value (object, array, or JSON string) | |
| original | Yes | Original JSON value (object, array, or JSON string) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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.
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.