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Velora LATAM Tools

Query sales

query_sales
Read-onlyIdempotent

Queries sales metrics from the database: 'ventas_periodo', 'margen', 'ranking_productos', 'por_empleado', or 'historial_cliente'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd date YYYY-MM-DD. Requires 'from'.
fromNoStart date YYYY-MM-DD. Requires 'to'.
limitNoMax results (default 10, max 50).
presetNoPreset date range. Mutually exclusive with from/to.
metricaYesMetric to query.
customer_nameNoCustomer name or fragment. Only for historial_cliente.

TDQS

B3.2/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description adds no safety-related behavioral context. Beyond that, the description merely lists metric names already present in the schema enum, and it does not disclose any additional behavior such as required date ranges, return formats, or metric-specific constraints.

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?

The description is a single, front-loaded sentence that effectively communicates the core purpose and enumerates the metric options without extraneous words. It is appropriately sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool handles multiple metrics with varying parameter requirements (e.g., customer_name for historial_cliente, mutually exclusive preset vs. from/to), but the description does not explain these relationships or what each metric returns. Since there is no output schema, the description should have provided more context on expected results or usage constraints, making it incomplete for a multi-metric 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?

Schema description coverage is 100%, and each parameter is documented there. The description's list of metric values adds little beyond the schema's own enum for 'metrica', so it meets the baseline but does not provide deeper semantic meaning for each metric or how parameters interact (e.g., customer_name only for historial_cliente).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Queries sales metrics') and the specific resource ('from the database'), and it enumerates the valid metric names, which differentiates it from sibling tools like query_catalog or caja_consultar_saldo. However, it does not explicitly contrast with those siblings, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for sales metric queries by listing the metric options, but it gives no explicit guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. The usage context is only implied through the metric names.

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

B3.3/5.0
Disambiguation2/5

Several tools duplicate the same underlying data in different presentation modes (get_payment_intent_status vs open_cobro_status, query_catalog vs open_catalog_selector, connection_status vs open_onboarding), creating ambiguity. Descriptions mention 'widget' vs JSON but an agent could easily pick the wrong one for a given task.

Naming Consistency2/5

Tool names mix Spanish and English (caja_consultar_saldo vs validate_cuit) and use varying verb prefixes (open_, query_, get_, list_, find_, track_, quote_, validate_); only the open_ prefix is consistent.

Tool Count3/5

22 tools sits at the high end of the borderline range; the server covers multiple domains (catalog, shipping, payments, fiscal) which justifies many tools, but some widgets and query equivalents could be consolidated.

Completeness2/5

The surface is heavily read-only: there are no create/update/delete tools for customers, suppliers, or products, and the only way to register a sale is through a widget that fires an internal function. There are also no tools to actually create a payment link or charge, leaving transactional workflows incomplete.

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