Query Argentina hydrocarbon trade balance (exports/imports). Covers crude oil (HS 2709), refined products (HS 2710), and natural gas (HS 2711). Values in FOB USD.
| Name | Required | Description | Default |
|---|---|---|---|
| anio | No | Filter by year | |
| flow | No | Trade flow direction | |
| producto | No | Product category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It adds useful behavioral context: values are in FOB USD, and specific HS codes are covered. However, it does not disclose response shape, default behavior with no filters, or other side effects/requirements, which would be valuable for a tool with no output schema.
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?
The description is three short sentences, with the main action and resource first. It avoids filler, and every sentence contributes either scope, content coverage, or units. This is concise and well-structured.
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?
Given there is no output schema and no annotations, the description still provides enough context for selection: resource, product coverage, flow direction, and valuation basis. Optional filters appear in the schema. It lacks explicit return-structure or usage examples, but for a straightforward query tool this is largely adequate.
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%, but the descriptions are minimal. The tool description adds meaningful semantics by mapping 'producto' values to HS codes and clarifying that values are in FOB USD, going beyond what the schema states. This helps the agent understand how to choose parameter values correctly.
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 a specific verb ('Query') and a clear resource ('Argentina hydrocarbon trade balance'), and enriches it with product scope (crude, refined, gas). This clearly distinguishes it from sibling tools like query_prices or query_production, so an agent can readily identify what this tool does.
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 implies usage by naming the exact data domain but provides no explicit when-to-use guidance or references to alternative tools. There are no exclusions or conditions stated, so the context is inferred rather than directly explained.
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.
