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eval_schema_compliance

Check that LLM-generated JSON conforms to a provided schema, reporting per-field validation errors for targeted fixes.

Instructions

Validate that an LLM output conforms to a JSON Schema.

Wraps multivon-eval's SchemaEvaluator. Parses the LLM output as JSON (tolerantly strips markdown code fences), then validates the parsed structure against the provided JSON Schema dict. Reports per-field validation errors — not just "valid/invalid".

For Pydantic-model validation or more advanced setups (custom validators, recursive schemas), use the multivon-eval SDK directly.

Args: output: The LLM-generated text expected to contain JSON. schema: A JSON Schema dict (Draft 7). Example: {"type": "object", "required": ["title", "score"], "properties": {"title": {"type": "string"}, "score": {"type": "number"}}}. strict: If True, additional fields not in the schema are treated as failures.

Returns: {"score": 0.0-1.0, "passed": bool, "reason": str, "threshold": float, "evaluator": "schema_compliance"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
outputYes
schemaYes
strictNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description carries full burden. It thoroughly explains behavior: tolerant parsing (strips markdown fences), per-field validation errors, strict mode effect, and the exact return format. No contradictions.

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 well-structured and front-loaded with the purpose. It is slightly lengthy due to parameter and return details, but each sentence adds value. Could be slightly more concise without losing clarity.

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

Completeness5/5

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, output schema exists), the description is complete. It explains the return format, error handling, and parameter semantics. No gaps remain for the agent to guess.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates fully with detailed parameter explanations: 'output' as LLM-generated text, 'schema' with a concrete JSON Schema example, and 'strict' with its boolean effect. This adds significant meaning beyond the schema.

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's purpose: 'Validate that an LLM output conforms to a JSON Schema.' It differentiates from sibling eval tools by focusing on schema compliance, and mentions internal details that help the agent understand its scope.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance is provided: 'For Pydantic-model validation or more advanced setups... use the multivon-eval SDK directly.' This tells the agent when not to use this tool, and the description explains the tool's capabilities (tolerant parsing, per-field errors) that help decide when to use it.

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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