Autario Data Analytics Platform

verify_value

Read-onlyIdempotent

Verify that a claimed value is correct. Use this when a user asks "did you hallucinate that?" or when you want to double-check your cited numbers before presenting. Pass the indicator, entity, time, and your expected value. Returns whether autario's live value matches, with relative difference and provenance. If your time= matches more than one observation (e.g. a year on a monthly series) you get reason="ambiguous_query" plus the candidate observations instead of a verdict: narrow time= and ask again rather than treating any single candidate as the answer.

Input Schema

TableJSON Schema

Name	Required	Description
`time`	Yes	Time period (e.g. "2023" or "2023-06")
`entity`	Yes	Entity code (e.g. DEU, USA, EUU)
`expected`	No	The value you want to verify. Omit for existence-only check.
`indicator`	Yes	Indicator ID

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent), the description discloses important behavioral details: it returns relative difference and provenance, and in ambiguous cases returns reason='ambiguous_query' with candidate observations instead of a verdict, along with a resolution instruction. This goes well beyond the annotation hints.

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 a single paragraph, front-loaded with purpose and usage, but includes a somewhat lengthy explanation of the ambiguous query scenario. It remains clear and structured, though not extremely terse.

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?

The description covers the return behavior (relative difference, provenance, ambiguous case handling) and provides guidance for resolving ambiguity. It does not mention error conditions or exact output format, but given that no output schema is defined, it is sufficiently complete for an agent.

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?

The input schema already provides 100% coverage of parameter descriptions (e.g., 'expected' says 'Omit for existence-only check'). The tool description merely restates the parameters without adding new semantic meaning, so it does not enhance the schema beyond the baseline.

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?

States a specific verb 'verify' and the resource 'value', clearly distinguishing it from general data retrieval tools like get_entity_data or query_dataset. The description also provides immediate context for when it applies (checking for hallucinated numbers).

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 specifies when to use the tool ('when a user asks did you hallucinate that? or when you want to double-check your cited numbers'), and gives handling instructions for ambiguous queries. This leaves no doubt about its appropriate use case.

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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