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Autario Data Analytics Platform

by com.autario

request_chart

AUTARIO-INTERNAL (admin only). HIGH-LEVEL chart request: you do NOT build a spec, but you DO write the insight. TWO-STEP FLOW for a first-try hit: (1) PREPARE - call with dataset_id/query and NO insight; the server composes the chart deterministically and returns charted_entities (the exact entity set it drew, each with latest/peak/trough/average) + chart_type, WITHOUT publishing. IMPORTANT: a multi-country dataset is charted as an ENTITY FAMILY (the top economies, G7, the aggregate rows...), so your insight is verified ONLY against the entities actually in charted_entities | anchor every claim on one of THOSE entities and cite only THOSE per-entity values. (2) PUBLISH - call again with the same dataset_id/query PLUS your 2-3 sentence insight; the server verifies it against the real data (number-hallucination gate) and publishes, returning the URL. The server runs NO LLM of its own (you write the insight). One request = one chart. On reject it returns 422 naming WHICH number/claim failed + the charted_entities + available anchors so you fix in one step. Use THIS over create_chart_from_spec whenever you want "a good chart for this dataset/topic" without assembling a full Builder spec. Non-admin keys receive 403; third parties use create_chart_from_spec / publish_chart.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timeNoOPTIONAL time-range hint (e.g. "2010-2024"). Soft preference; the server uses the actual data span.
queryNoFree-form topic/search string the server resolves to the best chartable dataset (e.g. "global inflation", "US unemployment rate"). Use instead of dataset_id when you only know the topic. dataset_id wins if both are given.
regionNoOPTIONAL hint to focus a multi-country dataset on a region/entity (e.g. "G7", "Europe"). Soft preference; the server picks the final entity set.
insightNoYour 2-3 sentence data insight. OMIT IT on the PREPARE call to receive charted_entities + anchors first; SEND IT on the PUBLISH call to verify + publish. Every cited number MUST be one of the per-entity values in charted_entities (latest/peak/trough/average) returned by the prepare call. The server verifies it against the real data and publishes on pass, or returns the failing number(s) + charted_entities + anchors on fail. The server does NOT write this for you.
chart_typeNoOPTIONAL hint (line | bar | snapshot). The server still owns the final chart-type decision based on the data shape; this is a soft preference only.
dataset_idNoUUID of the dataset to chart (from search_datasets / discover_by_topic / list_chart_candidates). Preferred when you already know the dataset.

TDQS

A4.9/5.0
Behavior5/5

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

Goes well beyond annotations (readOnlyHint false, openWorldHint true) by detailing administrative restriction, deterministic server-side charting, no LLM involvement, prepare/publish flow, entity verification, and 422 error response with actionable info.

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?

Every sentence provides necessary detail, but the description is long. It front-loads key information and is well-organized, though could be slightly more concise without losing clarity.

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 no output schema and the tool's complexity (two-step flow, entity family handling, verification), the description fully covers what the agent needs: return values, error handling, and usage steps. No gaps.

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?

Schema coverage is 100% and description adds valuable context: soft preferences for time, region, chart_type; the dual use of insight (omit on prepare, send on publish); and the precedence of dataset_id over query.

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's for high-level chart requests with a two-step flow. It distinguishes itself from sibling tool create_chart_from_spec by specifying when to use this tool over the alternative.

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?

Explicitly explains when to use (avoiding full spec) and when not (admin-only; third parties use other tools). Provides clear two-step procedure: prepare then publish, with details on each call's requirements.

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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