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serialize_data

Convert validated Pydantic models into serialized formats like JSON or Python dictionaries using configurable serialization options.

Instructions

Dump validated data using Pydantic serialization behavior.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
dataYes
output_modeNopython
by_aliasNo
exclude_unsetNo
exclude_defaultsNo
exclude_noneNo
round_tripNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNo
artifactsNo
diagnosticsNo
resolved_targetNo

Implementation Reference

  • The implementation of the serialize_data tool handler.
    def serialize_data(
        target: str,
        data: object,
        output_mode: str = "python",
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
    ) -> ToolResponse:
        """Dump validated data using Pydantic serialization behavior."""
        runtime_target = resolve_target(
            target,
            registry=REGISTRY,
            settings=SERVER_SETTINGS,
        )
        response = serialize_with_adapter(
            runtime_target,
            data=data,
            output_mode=output_mode,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
        )
        _record_response_errors("serialize_data", target, response)
        return response
Behavior1/5

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

No annotations are provided, so the description carries full burden. It mentions 'Pydantic serialization behavior' but doesn't explain what that entails - whether it's read-only, what permissions are needed, what format the output takes, or any error conditions. The description is too terse to provide meaningful behavioral context.

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 extremely concise at just 7 words. While this brevity comes at the cost of completeness, every word earns its place by mentioning key concepts like 'dump', 'validated data', and 'Pydantic serialization'.

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

Completeness1/5

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

For a tool with 8 parameters, no annotations, 0% schema coverage, and multiple sibling tools, the description is completely inadequate. While an output schema exists, the description doesn't explain what the tool does, when to use it, what the parameters mean, or any behavioral characteristics.

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

Parameters1/5

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

With 8 parameters and 0% schema description coverage, the description provides no information about any parameters. It doesn't explain what 'target', 'data', 'output_mode', or any of the boolean flags mean. The description fails to compensate for the complete lack of parameter documentation in the schema.

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

Purpose2/5

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

The description 'Dump validated data using Pydantic serialization behavior' is vague about what the tool actually does. It mentions 'dump' and 'serialization' but doesn't specify what resource is being serialized or what the output is. It doesn't clearly distinguish from siblings like 'validate_data' or 'generate_json_schema' which also deal with data processing.

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

Usage Guidelines1/5

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. With siblings like 'validate_data', 'generate_json_schema', and 'parse_partial_json' that also handle data transformation, there's no indication of when serialization is appropriate versus validation or schema generation.

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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