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Synthesize a data brief for a source, detailing critical issues, cleaning steps, modeling concerns, and suggested next actions to prepare data for modeling.

Instructions

Synthesize a paste-ready brief about source_id.

    Either runs `run_eda deep` fresh or, if `run_id` is provided, reuses that
    artifact. Returns a Markdown document by default with sections: Bottom
    Line · Critical Issues · Cleaning Steps · Modeling Concerns · Compliance &
    Sharing · What's Missing · How to Load · Suggested Next Actions.

    audience ∈ {auto, ml, analyst, engineer} — controls section ordering and
    emphasis. The Markdown is the primary output for chat UIs. The structured
    per-section breakdown is omitted by default (same content as `markdown`,
    doubles response size); set `include_sections=True` if you need it
    programmatically. The full structured data is always in the artifact.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintsNo
formatNomarkdown
run_idNo
audienceNoauto
source_idYes
include_sectionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses that it runs `run_eda deep` unless a `run_id` is supplied, stores full structured data in an artifact, and can double response size when sections are included. This provides meaningful insight into computational and output 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?

The description is dense but well-structured, front-loading the core purpose in the first sentence and using bullet-style section lists to convey details efficiently. Every sentence contributes meaningful information without verbosity.

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?

The tool has moderate complexity and no annotations, but the description covers execution flow, output sections, artifact storage, and audience customization. It omits explicit details about `format` values and sibling differentiation, but these are minor given the richness of the rest of the description.

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

Parameters3/5

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

The description adds semantic value to `source_id`, `run_id`, `audience`, and `include_sections` by explaining their roles and effects. However, `format` and `hints` are not elaborated, and with schema coverage at 0%, the partial compensation earns a mid-range score.

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 synthesizes a 'paste-ready brief' about `source_id`, with specific output sections. This distinct purpose sets it apart from sibling analysis tools like `describe_source` or `generate_report`, making it immediately actionable.

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

Usage Guidelines3/5

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

The description provides context for when to use `include_sections=True` (programmatic needs) and the `run_id` reuse option, but it does not explicitly compare against sibling tools or state when to prefer this over alternatives. Usage is implied rather than clearly delimited.

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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