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schema-tools

schema-tools

by rog0x
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

generate_json_schema

Generate a JSON Schema from sample JSON values to validate data structures. Infers types, required fields, and formats like email or UUID from multiple examples.

Instructions

Generate a JSON Schema from one or more sample JSON values. Infers types, required fields, formats (email, date, URI, UUID), and detects enum patterns from multiple examples.

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": {
      "samples": "<samples>"
    },
    "name": "generate_json_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleNoOptional title for the generated schema
samplesYesArray of sample JSON values to infer the schema from. More samples produce a more accurate schema.
detect_enumsNoWhether to detect enum values from string fields across samples (default: true, requires >= 3 samples)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of disclosing behavior. It states that it infers types, required fields, formats, and enum patterns, which is good. However, it does not discuss error handling (e.g., invalid JSON input), output format specifics (e.g., JSON Schema draft version), or potential incompleteness for complex schemas.

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 sentences that front-load the core purpose and immediately describe inferable features. No redundant words or irrelevant details.

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?

The description covers the main capabilities and parameter behavior well, given the tool's moderate complexity and complete parameter schema. It could mention output schema draft standard or validation of input samples, but these are minor gaps. The absence of an output schema is acceptable because the tool's output is a JSON Schema, which is self-describing.

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% – all parameters have descriptions. The description adds no parameter-specific detail beyond what the schema provides, but it does reinforce that more samples improve accuracy, which aligns with the samples parameter. Baseline 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 generates a JSON Schema from sample JSON values, with specific capabilities (type inference, required fields, formats, enums). This uniquely distinguishes it from siblings like generate_typescript (TypeScript generation) and validate_schema (validation).

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 purpose implies when to use it (when you have sample data and need a schema), but there is no explicit guidance on when not to use it or how it compares to alternative tools like generate_typescript. It does not mention any prerequisites or limitations on input samples.

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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