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pydantic-mcp

by BitingSnakes
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

compare_validation_modes

Compare Pydantic validation modes to understand how model, TypeAdapter, strict, and JSON-vs-Python validation handle data differently.

Instructions

Compare model, TypeAdapter, strict, and JSON-vs-Python validation behavior.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
dataYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNo
artifactsNo
diagnosticsNo
resolved_targetNo

Implementation Reference

  • src/pydantic_mcp/tools.py:223-235 (handler)
    The handler function `compare_validation_modes` which executes the tool by resolving the target and building a validation comparison.
    def compare_validation_modes(
    target: str,
    data: object,
) -> ToolResponse:
    """Compare model, TypeAdapter, strict, and JSON-vs-Python validation behavior."""
    runtime_target = resolve_target(
        target,
        registry=REGISTRY,
        settings=SERVER_SETTINGS,
    )
    response = build_validation_comparison(runtime_target, data=data)
    _record_response_errors("compare_validation_modes", target, response)
    return response

Tool Definition Quality

C2.6/5.0
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 mentions comparing validation behaviors but doesn't specify what the tool does (e.g., runs comparisons, outputs differences, requires specific inputs) or any behavioral traits like side effects, permissions, or rate limits. This is inadequate for a tool with no annotation coverage.

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

Conciseness4/5

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

The description is concise and to the point, using a single sentence without unnecessary words. However, it's under-specified rather than efficiently structured, as it lacks detail needed for clarity, but it's not verbose or poorly organized.

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 doesn't need to explain return values, but with no annotations, 0% schema coverage, and two required parameters, it's incomplete. It hints at comparison but fails to provide enough context for effective use, making it minimally adequate but with significant gaps.

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 description must compensate for undocumented parameters. It doesn't explain what 'target' or 'data' mean in the context of validation comparison, leaving their semantics unclear. The description adds no parameter-specific information beyond the vague purpose.

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

Purpose3/5

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

The description states the tool compares validation behaviors, which gives a general purpose, but it's vague about what specific resource or action is involved. It mentions 'model, TypeAdapter, strict, and JSON-vs-Python validation behavior' without specifying if this is for a particular framework or context, making it somewhat unclear but not tautological.

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. Given sibling tools like 'explain_model', 'validate_data', and 'inspect_type', there's no indication of how this comparison differs or when it's appropriate, leaving the agent without usage context.

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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