autario-mcp

get_app_artifact

Read-onlyIdempotent

Load ONE saved artifact from an autario data app | the EXACT view state the user saved there (a saved SEO 360 / Audience 360 / Social 360 / AI Visibility 360 report configuration, a Plotly chart spec, a 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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "app_id": "<app_id>",
      "slug": "<slug>"
    },
    "name": "get_app_artifact"
  }
}

Input 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", "projects", "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.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds substantial behavioral detail: sequential dependency on get_app_context/get_my_workspace, owner-gating and visibility of foreign private artifacts, honest truncation for large data with preserved counts, and the option to use query_dataset for raw data. These go well beyond the annotations, and no contradiction exists.

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 dense and information-rich, using pipe separators to break into logical chunks. It front-loads the core purpose and then adds contextual details. Although it is somewhat long, every sentence contributes value (access rules, truncation, alternatives). It is not overly redundant, so a 4 is warranted.

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 (3 params, no output schema), the description covers all essential operational aspects: when to call it (after listing artifacts), what it returns (saved view state plus inline data), limitations (owner-gating, truncation with notes), and alternatives (query_dataset for raw data). It also states read-only/no cost. An agent can invoke it correctly without further info.

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 all three parameters (slug, app_id, format) are already documented in the schema. The description adds little param-specific meaning; it refers to slug source ('from get_app_context / get_my_workspace') and app_id source ('from list_apps'), but those are already in the schema descriptions. Hence baseline 3 is appropriate.

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 states a specific verb and resource: 'Load ONE saved artifact from an autario data app'. It defines what an artifact is (EXACT view state, listing specific types) and distinguishes it from related tools by explaining it returns the saved configuration/data rather than a guess. It clearly separates from siblings like query_dataset and get_app_context.

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 explicitly says 'Call after get_app_context / get_my_workspace listed the artifact slugs', giving a clear sequence. It also names an alternative for full raw data: 'query the app's datasets via query_dataset', and explains visibility rules (owner-gated) plus the truncation caveat. This is explicit when-to-use and when-not-to-use guidance.

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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