Validates if data conforms to a JSON schema. Returns valid:true/false and an error message on failure.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Data to validate (any type) | |
| schema | Yes | JSON schema to validate against |
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 explicitly states the return format ('valid:true/false') and the inclusion of an error message on failure, which gives the agent a clear picture of expected outcomes. However, it does not elaborate on edge cases or side effects, though for a validator these are minimal.
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 states the core function and output. It contains no unnecessary words or repetition, earning a perfect score for conciseness and structure.
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 validator with two clearly documented parameters and a described output, the description is nearly complete. It lacks explicit usage scenarios or comparisons to siblings, but these are covered by other dimensions. The absence of an output schema is compensated by the inline mention of the return value, making it sufficiently 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% (both 'data' and 'schema' have descriptions). The description adds no extra meaning beyond what the schema already provides, so the baseline score of 3 is appropriate. It neither enriches nor contradicts the parameter definitions.
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 with a specific verb ('validates') and resource ('data conforms to a JSON schema'). It distinguishes from sibling tools by focusing on validation rather than analysis or detection, leaving no ambiguity about its purpose.
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?
No guidance is provided on when to use this tool versus alternatives like breaking-change-analyzer or drift-detector. The description implies usage only through its purpose, but there is no explicit mention of scenarios or exclusions, which is a notable gap.
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.