Autario Data Analytics Platform

get_chart

Read-onlyIdempotent

Get a specific chart by ID or slug. Returns a COMPACT, token-bounded summary (NOT the raw Plotly spec or full data arrays, which can be megabytes): title, insight/narration, datasets_used (with publisher), chart_type, the time/x range, and a PER-SERIES summary (first/latest/min/max/avg + point count, plus a small downsampled sample). For the full interactive chart and every data point, open view_url. The chart URL is shareable at autario.com/chart/{id}.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`format`	No	Output wire format for this MCP call. Default 'toon' (Token-Oriented Notation, fewest tokens, best for tabular rows). 'compact' = minified JSON. 'json' = pretty JSON for readability. The REST API always returns JSON regardless.
`chart_id`	Yes	The chart ID (numeric) or slug (hash like "nMGf-iAO") to retrieve

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds transparency by detailing the bounded nature of the response (no raw spec or full arrays), token-efficiency, and the per-series summary specifics beyond what annotations provide.

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 front-loaded with purpose and return summary, then details what is returned. It is slightly verbose but every sentence adds value. Minor trimming (e.g., repeated mention of 'compact') could improve conciseness.

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 2 parameters and no output schema, the description adequately explains the return structure, including what is not returned (raw spec, data arrays). It covers key aspects needed for an agent to understand the tool's capabilities and limitations.

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 coverage is 100% with clear descriptions. The description mentions 'toon' format but adds no semantic value beyond schema. It reinforces the format options but does not provide new constraints or examples.

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 the tool retrieves a specific chart by ID or slug, distinguishing it from list_charts (which lists all) and create_chart_from_spec (which requires spec input). It specifies the compact summary returned and what is excluded.

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 implies this tool should be used when a compact summary is sufficient, directing users to view_url for full interactive data. It does not explicitly exclude alternatives but provides clear context for appropriate 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.

Resources

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