Autario Data Analytics Platform

get_app_artifact

Read-onlyIdempotent

Load ONE saved artifact from an autario data app | the EXACT view state the user saved there (report configuration, chart spec, OKR board, screener view) plus any inline data, so your answer is grounded in what the user actually sees instead of a guess. Call after get_app_context / get_my_workspace listed the artifact slugs. Owner-gated: you see your own artifacts plus public/unlisted ones; foreign private artifacts are invisible. Very large specs/data are truncated honestly (marked with truncation notes; row/item counts stay correct) | for full raw data query the app's datasets via query_dataset. Read-only, no cost.

Input Schema

TableJSON Schema

Name	Required	Description
`slug`	Yes	Artifact slug from get_app_context / get_my_workspace (your_artifacts[].slug).
`app_id`	Yes	App id from list_apps, e.g. "audience-360", "okr", "builder".
`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.

TDQS

A4.9/5.0

Behavior5/5

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

Description adds behavioral details beyond annotations: truncation for large artifacts ('marked with truncation notes; row/item counts stay correct'), read-only and cost-free nature, and gating behavior. No contradiction with readOnlyHint=true and destructiveHint=false.

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?

Description is clear and front-loaded with purpose, but is slightly lengthy. Each sentence adds value, though some phrasing could be tighter without losing meaning.

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?

Given the tool's complexity (gating, truncation, format options, no output schema), the description covers all necessary aspects: what it returns, when to call it, parameter specifics, and limitations. References sibling tools for context.

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?

With 100% schema coverage, description still adds value: explains slug source, provides app_id examples, details format options (toon, compact, json) and their use cases, including REST API behavior.

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 loads 'ONE saved artifact' and specifies the content: 'report configuration, chart spec, OKR board, screener view' plus inline data. It distinguishes from siblings by referencing get_app_context/get_my_workspace for listing slugs.

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?

Explicit instruction to 'Call after get_app_context / get_my_workspace listed the artifact slugs.' Covers gating ('Owner-gated... foreign private artifacts are invisible') and alternative action for full data ('query the app's datasets via query_dataset').

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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