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save_review

Save an agent's work as a review file and log the run for dashboard visibility. Use after completing any substantive task to file and track results.

Instructions

Save an agent's output as a review file and record the run.

This is the standard way a Metis agent persists its work: it writes the
markdown to outputs/reviews/{agent_slug}/{date}_{task_slug}.md and, by
default, logs the run so the dashboard's Agents tab tracks it. Use it at the
end of any substantive agent task so the result is filed and discoverable.

Args:
    agent_slug: Slug of the agent that produced the review
        (e.g. "epidemiologist", "writing-partner").
    task_slug: Short kebab-case slug identifying the task; becomes part of
        the filename (e.g. "article1-methodology").
    content: The full review content as markdown.
    log_run: Whether to also record this as an agent run for the dashboard.
        Defaults to True.

Returns:
    A confirmation with the path of the saved review file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_slugYes
task_slugYes
contentYes
log_runNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description fully carries the burden. It discloses file path pattern (outputs/reviews/...), default logging behavior, and return value. Does not cover error states or overwrite behavior, but is transparent on key behaviors.

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?

Well-structured with a clear first sentence, followed by context, usage instruction, parameter documentation, and return description. No extraneous text, every sentence is informative.

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?

Covers purpose, parameters, usage, side effects, and return value. Output schema exists (implied by Returns section), and description complements it well, leaving no major gaps.

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?

The Args section provides meaningful examples ('epidemiologist', 'article1-methodology') and explains the role of each parameter, including the default for log_run. This adds substantial value beyond the input schema, which only provides type and title.

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 specifies the action 'Save an agent's output as a review file' and clearly distinguishes from sibling tools like log_agent_run and save_brainstorm_output by mentioning it is the standard way to persist work and filing results.

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?

Explicitly states 'Use it at the end of any substantive agent task' providing clear when-to-use guidance. Does not explicitly mention alternatives or when not to use, but context is strong.

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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