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Autario Data Analytics Platform

by com.autario

get_my_workspace

Read-onlyIdempotent

YOUR data-app workspace in ONE call: every autario app the calling user has activated or connected, each with its providers, connector-backed tables (dataset_id/slug + row count + last refresh), saved artifact list and a ready-to-run query example. THE first call when a user references "my ", "my dashboard", "my report" or asks what they have on autario | it replaces one get_app_context round-trip per app and guarantees you reason over the SAME datasets and saved views the user sees (no dataset guessing, no hallucinated numbers). Drill down with get_app_artifact(app_id, slug) for an exact saved view or query_dataset(dataset_id) for rows. Requires authentication (API key or OAuth). Read-only, no cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput 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

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true. The description adds context: requires authentication, no cost, guarantees consistency with user's view, and that it's read-only. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and packed with information, but it is a single long paragraph without bullet points or clear segmentation. Some redundancy (e.g., 'read-only, no cost' repeated). Could be more structured.

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?

For a tool with no output schema, the description thoroughly lists what is returned (apps, providers, tables with dataset_id/slug + row count + last refresh, artifacts, query example). Also covers authentication and cost. Very complete.

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?

Only one parameter (format) with enum values. The description explains the meaning of each format (toon for fewest tokens, compact vs json) beyond the schema, helping agents choose appropriately.

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 returns the user's workspace (data-apps, providers, tables, artifacts, query example) in one call. It distinguishes from sibling tools like get_app_context by explicitly mentioning it replaces multiple round-trips.

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?

Explicitly says 'THE first call when a user references "my <app>"' and provides concrete use cases. It also guides to drill-down tools (get_app_artifact, query_dataset) for further details, though it does not explicitly state when not to use this tool.

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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