Query upstream oil & gas investments in Argentina by company, basin, and year. Data includes drilling, completion, and infrastructure expenditure in millions USD.
| Name | Required | Description | Default |
|---|---|---|---|
| cuenca | No | Basin filter | |
| empresa | No | Company filter | |
| concepto | No | Investment concept (e.g. perforacion, terminacion, infraestructura) | |
| anio_desde | No | Start year | |
| anio_hasta | No | End year |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It adds useful context that values are in millions USD and covers drilling, completion, and infrastructure. However, it does not state the return format, whether results are aggregated, or any effect of leaving all filters empty, which is a moderate gap for a query tool.
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 with no fluff. The first sentence states the action, resource, and filters; the second conveys scope and units. Every word earns its place.
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 tool has 5 optional parameters and no output schema, so the description should clarify what the result set looks like. It gives units and categories but does not mention aggregation, pagination, or whether any filter is required. This is a meaningful gap, but the tool's scope is reasonably clear.
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 covers 100% of parameters with descriptions, so this baseline is adequate. The description adds context about the domain and units but does not add syntax-level details beyond what the schema already provides. It aligns with the 'concepto' examples but does not materially improve parameter understanding.
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 names the exact resource: upstream oil & gas investments in Argentina, with the key filter dimensions (company, basin, year). It clearly distinguishes this from sibling tools by domain (investments vs. prices, production, etc.).
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 clearly implies when this tool should be used (for investment data in Argentina) and sets context by listing included expenditure categories. However, it does not explicitly name alternatives or state when not to use it, though sibling tool names make the distinction implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
