Autario Data Analytics Platform

report_data_issue

Idempotent

Report a data-quality problem you found in a dataset or chart, so the engine can fix it. Use this during a QA pass when you spot: a dataset that looks truncated / only partially ingested (far fewer rows than the source should have), a unit that contradicts the value range (unit "%" but values in the thousands), nonsensical or wrong column/series labels, an all-identical (zero-variance) column, a published chart that is misleading or plots the wrong series, or data that looks stale. ALWAYS attach the concrete numbers you observed in evidence (e.g. the row count you saw vs. what you expected, the unit, a few sample values) | findings without evidence are not actionable. The engine routes safe types (partial_ingest_suspected, stale, broken_time_col) to an automatic re-ingest on the next refresh; everything else goes to a human review queue. Reporting the same open issue twice is a harmless no-op (deduped). Requires authentication.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`detail`	No	One-sentence human-readable summary of the issue.
`evidence`	No	The concrete numbers backing the finding, as a JSON object. Examples: {"rows_seen": 500, "rows_expected": 15000, "source": "World Bank API has ~15k country-year rows"} or {"unit": "%", "value_range": [120, 9800]}. Required for an actionable finding.
`severity`	No	How bad it is for end users. high = wrong/misleading numbers shown publicly. Default medium.	medium
`dataset_id`	No	The UUID of the dataset the issue is about (from search_datasets / get_dataset_info). Omit only for a chart-level issue with no single owning dataset.
`finding_type`	Yes	What kind of problem. partial_ingest_suspected = fewer rows than the source has (truncated). stale = data older than it should be. broken_time_col = every row shares one date / a vintage column is used as time. unit_mismatch = declared unit contradicts the numbers. label = wrong/nonsensical column or series names. wrong_series = the wrong or a duplicate series is shown. confusing_chart = a published chart is misleading to end users. zero_variance = all values identical. engine_gap = a systematic parser/engine bug. moved/dead_source = source URL changed or returns 404.

TDQS

A4.6/5.0

Behavior5/5

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

Beyond annotations (idempotentHint, not readOnly), the description adds that authentication is required, that duplicate reports are no-ops, and explains routing behavior (auto re-ingest vs. human review). It fully discloses the tool's effects without contradicting annotations.

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

Conciseness5/5

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

The description is moderately long but every sentence earns its place. It starts with a clear one-line purpose, lists use cases, gives a critical instruction about evidence, and ends with routing and dedup details. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (5 params, nested objects, no output schema), the description covers purpose, usage, parameter guidance, authentication, and routing. It lacks detail on the return value (e.g., status or issue ID), but this is minor for a reporting tool.

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?

With 100% schema coverage, the description adds little beyond what the schema already provides. It reinforces the importance of 'evidence' and gives usage examples, but most parameter details are already in the schema. 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 clearly states 'Report a data-quality problem you found in a dataset or chart' with a specific verb and resource. It lists concrete scenarios and distinguishes itself from sibling tools like 'create_dataset' or 'update_chart' which do not involve reporting issues.

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 states 'Use this during a QA pass when you spot:' and enumerates specific conditions. It also instructs to always attach evidence and notes that reporting the same issue twice is harmless, providing clear when-to-use and when-not-to-worry 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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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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