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brief_validate

Validate a brief payload to check if it can be saved or promoted, returning normalized data and structured errors without persisting the input.

Instructions

Validate a public Nogra brief payload without persisting it.

    Parses and normalizes the supplied payload, then runs the public brief schema checks. Invalid input is returned
    as structured errors rather than raised to the caller.

    When to use:
    - Check whether a draft can be saved or promoted before writing it.
    - Inspect the normalized public brief shape produced from markdown.

    When NOT to use:
    - Do not use this to persist a draft; use brief_save for writes.
    - Do not use this to read stored briefs; use brief_read or recent_briefs.

    Examples:
    >>> brief_validate({"title": "Import audit", ...})
    {"valid": true, "errors": [], "normalized": {...}}
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYesBrief payload as markdown, JSON string, or structured object.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, so the description fully covers behavior: it does not persist, returns structured errors instead of raising, and normalizes input. The example clarifies the return shape, compensating for missing annotations.

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 well-organized with clear sections, no wasted words, and front-loaded with the core purpose. Every sentence serves a purpose.

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?

For a validation tool with one parameter and an output schema, the description is complete. It explains the logic, error handling, and provides an example, leaving no gaps for an agent.

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

Parameters4/5

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

Schema coverage is 100% with description for payload. The description adds context about parsing and normalization, but the schema already specifies types. The example provides additional clarity, warranting a score above baseline.

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 validates a brief payload without persisting it, using specific verbs like 'validate' and 'normalize'. It distinguishes from siblings like brief_save, brief_read, and recent_briefs.

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?

Explicitly provides when to use (check before save/promote, inspect normalized shape) and when NOT to use (not for persisting or reading), referencing sibling tools directly.

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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