Detects differences between an actual API response and an expected JSON schema. Reports missing_required (error), type_mismatch (error), enum_violation (error), missing_optional (warning), unexpected_field (info). Recurses into nested objects.
| Name | Required | Description | Default |
|---|---|---|---|
| schema | Yes | Expected JSON schema | |
| response | Yes | Actual API response to check against schema |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context by specifying the exact output categories and their severity levels (error/warning/info), and it mentions recursion into nested objects. While it doesn't detail the output format or error handling, the core behavior is well covered.
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 exactly two sentences. The first sentence states the high-level purpose, and the second condenses the behavioral details into a tight list of report categories plus recursion. There is no redundancy or filler, making it exceptionally concise and well-structured.
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?
Given the tool has simple parameters and no output schema, the description reasonably covers the essential context: what it does, what it detects, and how deeply it traverses. The only gap is the exact return format, but the enumerated report categories give a strong indication of the output. This is adequate for the tool's complexity.
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 100% description coverage for both parameters ('Expected JSON schema' and 'Actual API response to check against schema'). The description merely reinforces these concepts without adding new usage details, so a 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?
The description clearly states the tool's purpose: 'Detects differences between an actual API response and an expected JSON schema.' It uses a specific verb+resource structure and further distinguishes itself from siblings by enumerating the exact categories of differences reported (missing_required, type_mismatch, etc.), which aligns with the drift-detection focus.
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 clear context for when to use this tool—whenever there is a need to compare an actual API response against an expected schema and detect discrepancies. However, it does not explicitly mention alternatives like schema-validator or breaking-change-analyzer, nor does it state when not to use it. Thus it earns a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct aspect: schema version comparison, actual vs expected response checking, and general data validation. No overlap in purpose.
All tool names follow a consistent pattern: [domain concept]-[tool type] (e.g., breaking-change-analyzer, drift-detector, schema-validator), all lowercase with hyphens.
Three tools is appropriate for the focused domain of schema drift detection, covering the main operations without being too few or excessive.
The tool set covers schema version comparison, API response drift detection, and schema validation, providing a complete workflow for drift detection without obvious gaps.