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migrate_v1_to_v2

Analyze Pydantic v1 code to identify migration issues for v2 and optionally apply fixes.

Instructions

Analyze a snippet or model source for common Pydantic v1-to-v2 migration issues.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNo
targetNo
apply_fixesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNo
artifactsNo
diagnosticsNo
resolved_targetNo

Implementation Reference

  • The main handler function for migrate_v1_to_v2 which processes code or target model for migration issues.
    def migrate_v1_to_v2(
        code: str | None = None,
        target: str | None = None,
        apply_fixes: bool = False,
    ) -> ToolResponse:
        """Analyze a snippet or model source for common Pydantic v1-to-v2 migration issues."""
        if code is None and target is None:
            return make_response(
                diagnostics=[
                    Diagnostic(
                        level="error",
                        message="Provide either `code` or `target`.",
                        code="missing_input",
                    )
                ],
                result={"findings": [], "risk_level": "none", "updated_code": None},
            )
        if code is None and target is not None:
            runtime_target = resolve_target(
                target,
                registry=REGISTRY,
                settings=SERVER_SETTINGS,
            )
            code = inspect.getsource(
                runtime_target.model_class or runtime_target.annotation
            )
            response = migration_report(code, apply_fixes=apply_fixes)
            response.resolved_target = runtime_target.resolved
            return response
        return migration_report(code or "", apply_fixes=apply_fixes)
  • Registration of the migrate_v1_to_v2 tool in the server module.
    migrate_v1_to_v2 = _tools.migrate_v1_to_v2
    parse_partial_json = _tools.parse_partial_json
    generate_model_from_json = _tools.generate_model_from_json
    server_capabilities = _resources.server_capabilities
    models_index = _resources.models_index
    model_metadata = _resources.model_metadata
    model_schema = _resources.model_schema
    model_examples = _resources.model_examples
    reference_overview = _resources.reference_overview
    project_settings = _resources.project_settings
    project_import_roots = _resources.project_import_roots
    recent_errors = _resources.recent_errors
    migration_rules = _resources.migration_rules
    changed_models = _resources.changed_models
    
    __all__ = [
        "SERVER_NAME",
        "SERVER_SETTINGS",
        "SERVER_VERSION",
        "build_capabilities",
        "changed_models",
        "compare_validation_modes",
        "create_example_payload",
        "explain_model",
        "generate_json_schema",
        "generate_model_from_json",
        "inspect_type",
        "list_models",
        "main",
        "mcp",
        "migrate_v1_to_v2",
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. While it mentions analysis for migration issues, it doesn't specify whether this is a read-only operation, what the output format is (though an output schema exists), potential side effects, or performance considerations. For a tool with 3 parameters and no annotation coverage, this is insufficient.

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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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's complexity (3 parameters, migration analysis), lack of annotations, and 0% schema coverage, the description is incomplete. It doesn't explain parameter usage or behavioral details. However, the presence of an output schema reduces the need to describe return values, preventing a lower score.

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 'code', 'target', or 'apply_fixes' mean, their expected formats, or how they interact. The description adds no parameter semantics beyond the schema, failing to address the coverage gap.

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: 'Analyze a snippet or model source for common Pydantic v1-to-v2 migration issues.' It specifies the verb (analyze), resource (snippet/model source), and scope (Pydantic v1-to-v2 migration issues). However, it doesn't explicitly differentiate from sibling tools like 'compare_validation_modes' or 'explain_model', which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for migration analysis, or how it differs from siblings like 'inspect_type' or 'validate_data'. This leaves the agent without clear usage instructions.

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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