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publish_chart

Publish a chart visualization to Autario using Plotly specs with column references. Autario pulls real data from datasets for accuracy, making charts permanent and shareable.

Instructions

Publish a new chart visualization to Autario. Requires a Plotly spec with column references (x_col, y_col, group_by, group_value). Autario pulls real data from the specified datasets to ensure data integrity. The chart becomes permanent, shareable, and editable at autario.com. Requires AUTARIO_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesChart title. Include time range in parentheses, use pipe | as separator (e.g. "GDP Growth | Major Economies (2000-2024)")
plotly_specNoPlotly specification with traces array and layout object. Traces use x_col/y_col for column references and group_by/group_value for filtering (e.g. {"traces": [{"x_col": "year", "y_col": "value", "group_by": "country", "group_value": "USA"}], "layout": {}})
insightNo2-3 sentence data insight with specific numbers from the queried data. Must use verified numbers from query_dataset results, never from training data
narrationNoLonger description of the analysis methodology and context
dataset_idsYesArray of dataset UUIDs that this chart uses. Autario pulls real data from these datasets to ensure no hallucinated values
Behavior4/5

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

Annotations already indicate readOnlyHint=false and destructiveHint=false. The description adds that charts become permanent, shareable, editable, and that Autario pulls real data at publish time. No contradiction.

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?

Four sentences covering purpose, requirements, behavior, and environment with no fluff. Front-loaded with the main action.

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 complexity (5 params, nested objects, no output schema), the description covers key aspects: Plotly spec, datasets, permanence, and auth. Missing explicit mention of return value or error handling.

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?

Schema description coverage is 100%, so the baseline is 3. The description re-emphasizes the Plotly spec column references but adds minimal new 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 uses the specific verb 'publish' with the resource 'new chart visualization to Autario', clearly distinguishing it from siblings like get_chart (retrieve) and update_chart (modify).

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 mentions requirements (Plotly spec, AUTARIO_API_KEY) and data integrity, but does not explicitly state when to use this tool versus alternatives or when not 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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