Energetica — Argentine Oil & Gas Data

query_production

Query monthly oil, gas, and water production data from Argentine hydrocarbon wells. Data covers 2006-present with ~18M records. Can be filtered and grouped by basin, company, geological formation, and time period.

Input Schema

TableJSON Schema

Name	Required	Description
`cuenca`	No	Basin name filter (e.g. NEUQUINA, GOLFO SAN JORGE, CUYANA, AUSTRAL, NOROESTE)
`empresa`	No	Operating company filter (e.g. YPF, PAN AMERICAN ENERGY, VISTA, TECPETROL)
`recurso`	No	Resource type filter. Default: todos
`formacion`	No	Geological formation filter (e.g. VACA MUERTA, D-129, CENTENARIO)
`agrupar_por`	No	Group results by dimension. Default: mes
`fecha_desde`	No	Start date YYYY-MM (e.g. 2020-01)
`fecha_hasta`	No	End date YYYY-MM (e.g. 2025-12)

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the disclosure burden. It adds useful behavioral context (data coverage 2006-present, ~18M records, supports filtering/grouping), but it does not disclose response format, pagination, or performance characteristics.

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?

Two sentences with clear front-loading: the first names the operation and resource, the second provides scope and capabilities. No filler words or redundancy.

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 7-parameter schema and no output schema, the description provides sufficient context for a typical query task: it names the data domain, coverage, and available filters/groupings. It does not state the exact return structure, but that is a minor gap for this type of data query 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?

The input schema has 100% description coverage for all 7 parameters, so the baseline is 3. The description does not add parameter-specific semantics beyond what the schema already provides.

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 uses a specific verb ('Query') and names the exact resource (monthly production data from Argentine hydrocarbon wells). It clearly distinguishes from sibling query_* tools targeting investments, prices, trade, and wells.

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?

The description conveys clear usage context: this is the tool for production data, with filtering and grouping options. It does not explicitly name alternatives or exclusions, but the context is unambiguous for an agent deciding which tool to invoke.

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

A4.2/5.0

Disambiguation5/5

Each tool targets a distinct purpose: metadata (get_schema, get_data_freshness), generic SQL access (execute_sql), and domain-specific queries (investments, prices, production, trade, wells). There is no overlap between specialized queries, and execute_sql is clearly positioned as a raw fallback.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: get_ for metadata, query_ for data retrieval, and execute_sql for the raw query tool. The style is uniform and predictable, making it easy to infer tool behavior from names.

Tool Count5/5

With 8 tools, the set is well-scoped for the domain. It covers the core data dimensions (investments, prices, production, trade, wells) plus essential support tools (schema, freshness, raw SQL) without unnecessary bloat or redundancy.

Completeness5/5

The tool set covers all major facets of Argentine oil & gas data: production, investment, pricing, trade, and wells. The inclusion of execute_sql and get_schema ensures that any data not exposed via a dedicated query can still be accessed, leaving no obvious dead ends.

Resources

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