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Metis · Data Guardian — Validate Pipeline Stage

validate_pipeline_stage

Validates sub-agent output to ensure required keys are present and non-empty before passing to the next pipeline stage.

Instructions

M5.7.4 — Validate a sub-agent output before passing to the next pipeline stage.

Treats sub-agent output with the same suspicion as external tool output.
Rejects if any required keys are missing or empty.

Args:
    output_json: JSON string of the sub-agent output dict.
    required_keys: Comma-separated list of required keys (e.g., "title,summary,agent_slug").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_jsonYes
required_keysYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that it rejects missing or empty keys and treats output with suspicion. However, it does not state whether the tool is read-only or has side effects, but the validation action implies no mutation.

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 appropriately concise with a clear first sentence followed by a brief explanation and Args section. The inclusion of version number 'M5.7.4' is minor noise but does not detract significantly.

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

Completeness4/5

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

Given the presence of an output schema, the description does not need to detail return values. It covers the tool's purpose, parameter semantics, and validation behavior, making it reasonably complete for a simple validation tool.

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?

With 0% schema description coverage, the description compensates by explaining both parameters: output_json is a JSON string of the sub-agent output dict, and required_keys is a comma-separated list. This adds meaning beyond the schema types.

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 it validates a sub-agent output before passing to the next pipeline stage, using specific verb 'validate' and resource 'pipeline stage'. It distinguishes from siblings by focusing on validation between stages, which is unique among the listed tools.

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

Usage Guidelines4/5

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

The description explicitly says to use it before passing output to the next stage and treats output with suspicion like external tool output. It does not mention when not to use or list alternatives, but the context is clear.

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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