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Autario Data Analytics Platform

by com.autario

create_chart_from_spec

PREFERRED chart-creation path. Send a structured Builder spec (chart_type + x_col + y_col[s] + optional group_by, palette, axis overrides, annotations) and Autario builds the chart with the same templates the Builder UI uses. Brand attribution (publisher source + autario.com) is applied automatically and cannot be overridden. Insight must cite numbers verifiable against the data | hallucinated numbers return 422 with the available anchor list. For advanced use cases the Builder cannot express, fall back to publish_chart with a freeform plotly_spec. Call chart_instructions() first if unsure of the spec shape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoChart title (also settable via builder_spec.title; this top-level wins if both set). Format: "{Topic} | {Scope} ({YYYY-YYYY}, {unit})". The YYYY-YYYY year range is REQUIRED whenever the chart has a time axis (pull from the actual data span you queried). The unit is REQUIRED whenever get_dataset_info → unit is a non-empty string (copy verbatim, e.g. "Mt CO2e", "% of GDP", "per 1,000 live births"). If get_dataset_info → unit is null/empty, omit the unit | NEVER invent one. Example with both: "Greenhouse Gas Emissions by Country (2010-2024, Mt CO2e) | World Bank". Example unit-only: "Infant Mortality by Race (per 1,000 live births) | NCHS".
insightNo2-3 sentence data insight using ONLY numbers from query_dataset/get_dataset_schema results. Hallucinated numbers are rejected with the available anchor list.
narrationNoLonger description (optional, defaults to insight)
dataset_idsYesUUID array of datasets backing this chart. Autario pulls real data from these tables.
builder_specYesStructured Builder spec. Required: chart_type + x_col + y_col/y_cols (axis charts), label_col + value_col (pie/donut), x_col + group_by + value_col (heatmap). Optional: group_by, group_values, facet_by/facet_values (donut grid), heatmap_scale, title, palette/color_scheme, axis (x_title, y_title, y_min, y_max, log_scale, y_format, tick_angle, x_date_format), annotations, event_bands, overlays, legend_pos, bg_color, font_color, chart_height. See chart_instructions() for full reference.

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses key behavioral traits beyond annotations: automatic brand attribution that cannot be overridden, and validation of insight numbers leading to a 422 error if hallucinated. These details about irreversible attribution and error handling add significant value beyond the structured 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 concise (4 sentences), front-loading the primary purpose ('PREFERRED chart-creation path'), then efficiently covering constraints and fallback. Every sentence contributes essential information without redundancy.

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 description covers key aspects: purpose, preferred status, attribution, input validation, and fallback. However, it lacks details about the return value or output format (e.g., does it return a chart ID?), which would be helpful given the absence of an output schema. Overall, it is mostly complete but has a minor gap.

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?

Although the input schema has 100% coverage and detailed parameter descriptions, the description adds extra context: it explains that the top-level title overrides builder_spec.title, and references chart_instructions() for full builder_spec details. This provides additional semantic value beyond the schema alone.

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 is the 'PREFERRED chart-creation path' that builds charts from a structured Builder spec, distinguishing itself from the sibling tool publish_chart. It specifies the verb 'create' and resource 'chart' with a specific method (Builder spec), and explicitly contrasts with the fallback path for advanced use cases.

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?

The description provides explicit guidance: it is the preferred path, with a fallback to publish_chart for advanced cases, and recommends calling chart_instructions() first if unsure. This covers when to use the tool, when not to, and what alternative to use.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.

Naming Consistency4/5

The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.

Tool Count2/5

48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.

Completeness4/5

The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.

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