Autario Data Analytics Platform

get_app_context

Read-onlyIdempotent

The data map behind ONE autario app, so you can query app-first instead of guessing across thousands of datasets. Returns the app manifest (what it consumes, which connector providers it reads) and, for an authenticated caller, YOUR OWN reality behind it: your connector-instance tables (per-operation table with column list, row count, backing dataset_id and last refresh), your saved artifacts in the app, and 2-3 ready-to-run query examples on existing endpoints (query the dataset_id with query_dataset or GET /datasets/:id/data). Secrets and credentials are never included. Unauthenticated callers get the public manifest view. Use when a user asks "what does my run on", "what data is behind ", "query my Search Console data" (Audience 360), or before analyzing any app-connected data. app ids come from list_apps.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`app_id`	Yes	App id from list_apps, e.g. "audience-360", "company-compare", "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

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint; description adds that secrets are never included and differentiates behavior for authenticated vs unauthenticated callers, adding value beyond annotations.

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?

Front-loaded with key purpose, each sentence adds value, though slightly wordy. Could be tightened but effective.

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?

No output schema but description comprehensively details return data (manifest, tables, artifacts, examples) and accounts for authentication state, making it complete for its purpose.

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%, so baseline is 3. Description references app_id source and format but doesn't add significant new meaning beyond schema descriptions.

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 returns the data map behind a single app, including manifest, tables, artifacts, and query examples. It distinguishes itself from sibling tools by being app-specific.

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?

Provides explicit example queries like 'what does my <app> run on' and mentions where app_ids come from. Lacks explicit when-not-to-use guidance but context is clear.

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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