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Specwise

by hybridforgelab
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Python
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validate_json_schema

Read-onlyIdempotent

Validate a JSON Schema against its meta-schema and optionally validate data against it, returning errors with instance and schema paths plus line numbers for text input.

Instructions

Check a JSON Schema against its meta-schema and, if given, validate data against it. Every error has the instance path, the schema path and, for text input, the line.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "validate_json_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
draftNoForce a draft; by default from $schema, else 2020-12.
schemaNoThe JSON Schema, as JSON or YAML text (or a JSON object).
instanceNoData to validate: JSON or YAML text, or the value itself. Optional.
schema_pathNoA local file instead of `schema`.
instance_pathNoData to validate, from a local file.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), and the description adds genuinely new behavioral context: it discloses that every error carries an instance path, a schema path, and, for text input, a line number. It stops short of describing overall result shape or how errors are ordered/aggregated, so it is helpful but not complete.

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?

Two tight sentences with no filler: the primary purpose is front-loaded, the optional second mode follows, and the error-reporting detail closes it out. Every clause carries information.

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

Completeness4/5

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

With five all-optional parameters, no output schema, and full annotation coverage, the description is close to sufficient, and the error-structure sentence compensates partially for the missing output schema. The remaining gap is that it never explains conflict behavior between the paired parameters (schema vs schema_path, instance vs instance_path) or the draft inference fallback beyond what the schema's default note says.

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?

Schema coverage is 100%, so every parameter is documented in the schema itself, which sets the baseline at 3. The description only reinforces the schema/instance duality and the text-vs-object input forms already stated in the parameter descriptions, adding no new semantics about precedence when both schema and schema_path (or instance and instance_path) are supplied.

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

Purpose4/5

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

The description gives a specific verb+resource pair ('Check a JSON Schema against its meta-schema') plus the secondary mode ('validate data against it'), so the agent knows exactly what the tool does. It never names its siblings validate_openapi or diff_openapi, leaving the agent to infer differentiation from the names alone.

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?

Usage is only implied: the description notes that data validation happens 'if given', which hints at the two modes but never states when to prefer this tool over validate_openapi or when a schema-only check is appropriate. No prerequisites, exclusions, or alternative routing are provided.

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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