Query international and local energy prices. Includes WTI, Brent, Henry Hub (daily), Argentine crude prices (Escalante, Medanito), and exchange rates (official, blue, MEP, CCL).
| Name | Required | Description | Default |
|---|---|---|---|
| serie | Yes | Price series to query | |
| frecuencia | No | Frequency aggregation. Default: diario for intl prices, mensual for local | |
| fecha_desde | No | Start date YYYY-MM-DD | |
| fecha_hasta | No | End date YYYY-MM-DD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It clarifies the types of data included and mentions frequency defaults (daily for Henry Hub, implied for others), but does not describe return format, pagination, or default date behavior. As a query tool, read-only behavior is implicit but not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, followed by a concise list of included series. No redundant or filler content; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately explains the tool's scope and key series. Given that there is no output schema, it could elaborate on return structure, but the description is sufficient for understanding what data can be queried and the default frequency behavior. It is more complete than a minimal stub.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing baseline 3. The description adds value by grouping the series list into categories (international, local, exchange rates) and noting that Henry Hub is daily, which complements the schema's enum values. It also reinforces default frequency behavior for local prices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Query' and clearly defines the resource as 'international and local energy prices'. It enumerates specific series (WTI, Brent, Henry Hub, Argentine crude, exchange rates), distinguishing it clearly from sibling tools like query_production or query_trade.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (when needing energy prices or exchange rates listed) without explicitly naming alternatives. It does not include exclusions or when-not-to-use guidance, but the scope is well-defined enough to guide selection.
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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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.
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.
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.
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.
