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generate_json_schema

Generate JSON Schema for Pydantic models or Python types to validate data structures and enable interoperability between systems.

Instructions

Generate JSON Schema for a model or type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
schema_modeNovalidation
include_definitionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNo
artifactsNo
diagnosticsNo
resolved_targetNo

Implementation Reference

  • The handler function 'generate_json_schema' registered as an MCP tool, which resolves the target model and uses 'build_schema_report' to generate the JSON schema.
    @mcp.tool(tags={"schema", "pydantic"})
    def generate_json_schema(
        target: str,
        schema_mode: str = "validation",
        include_definitions: bool = True,
    ) -> ToolResponse:
        """Generate JSON Schema for a model or type."""
        runtime_target = resolve_target(
            target,
            registry=REGISTRY,
            settings=SERVER_SETTINGS,
        )
        return build_schema_report(
            runtime_target,
            schema_mode=schema_mode,
            include_definitions=include_definitions,
        )
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on permissions, rate limits, output format (though output schema exists), or error handling. For a tool with no annotations, this is a significant gap in transparency.

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 a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, clearly stating the core functionality without unnecessary elaboration.

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

Completeness3/5

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

Given the tool has an output schema, the description does not need to explain return values. However, with no annotations, 3 parameters (0% schema coverage), and no usage guidelines, the description is minimally adequate but leaves clear gaps in understanding how to use the tool effectively.

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

Parameters2/5

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

Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description does not explain what 'target', 'schema_mode', or 'include_definitions' mean, their expected values, or how they affect the generation. It fails to compensate for the lack of schema documentation.

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 clearly states the tool's purpose: 'Generate JSON Schema for a model or type.' It specifies the action (generate) and resource (JSON Schema), though it doesn't explicitly differentiate from siblings like 'inspect_type' or 'explain_model' that might also relate to models/types. The purpose is clear but lacks sibling distinction.

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

Usage Guidelines2/5

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. The description does not mention prerequisites, context, or exclusions, such as how it differs from 'validate_data' or 'create_example_payload' in the sibling list. Usage is implied only by the purpose statement.

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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