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schema-tools

schema-tools

by rog0x
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

validate_schema

Validate data against a JSON Schema to identify violations, receiving detailed errors with JSON path, expected type, actual value, and the violated keyword.

Instructions

Validate data against a JSON Schema. Returns detailed error messages with JSON path, expected type, actual value, and the violated keyword (type, required, format, pattern, minimum, etc.).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "schema": "<schema>",
      "data": "<data>"
    },
    "name": "validate_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYesThe data to validate
schemaYesThe JSON Schema to validate against

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Because no annotations are provided, the description carries the full burden of behavioral disclosure. It goes beyond a basic statement by explaining what the return value contains: 'detailed error messages with JSON path, expected type, actual value, and the violated keyword.' This gives the agent a clear picture of the tool's behavior and output format, though it does not mention possible side effects (e.g., that it is read-only).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose and followed by a concise explanation of the return value. Every word earns its place; there is no repetition of schema details or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a validation tool with two well-documented parameters and no output schema, this description is complete. It explains both the action and the nature of the response, so an agent knows exactly what to expect and does not need additional context to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% description coverage for the two parameters (data and schema). The description does not add specific parameter-level meaning beyond what the schema states, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Validate data against a JSON Schema.' This is a specific verb and resource, and it is immediately distinguishable from sibling tools like generate_json_schema or diff_schemas. The description also mentions the detailed error output, which further clarifies its role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly say when to use this tool versus alternatives, nor does it mention exclusions. However, the name and purpose make the use case obvious, and the sibling tools are distinctly different. The usage context is implied rather than explicitly stated.

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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