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by zenskar

createAggregate

createAggregate

Creates a new billable metric by specifying its name, data schema, aggregation queries, and data source.

Instructions

Create a new Billable Metric. Requires a name, data schema, aggregation queries, visual query builder config, and a data source. Host enforces user confirmation via the approval gate; do NOT ask the user to re-confirm before calling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the billable metric (required).
dataschemaYesData schema of the aggregate (JSON string, required).
aggregation_queryYesAggregate query — SQL/Jinja template for computing the metric (required).
cust_agg_queryYesCustomer aggregate query — SQL/Jinja template for customer-level computation (required).
visual_query_builderYesVisual query builder configuration object (required).
datasourceYesData source UUID (required).
__userContextNoInternal user context for multi-tenant authentication and approval workflow
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the approval gate but does not address side effects, required permissions, idempotency, rate limits, or what happens on conflict. The description is insufficient for an agent to understand the full behavioral impact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading the purpose and then a key usage note. Each sentence earns its place, and there is no extraneous text. Could be slightly more structured but overall efficient.

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?

Given the tool has 7 parameters, nested objects, and no output schema, the description is incomplete. It does not describe the return value, error conditions, prerequisites, or the behavior of the approval gate beyond a single directive. An agent would need more context to handle this tool confidently.

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%, so the baseline is 3. The description adds a list of required components (name, data schema, etc.), but this largely duplicates the schema parameter descriptions. It does not explain semantics beyond what the schema provides, such as the structure of visual_query_builder or acceptable values for datasource.

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 states 'Create a new Billable Metric' which is a clear verb+resource. However, it does not distinguish from sibling tools like createRawMetric, which also creates a metric-like entity. The list of required components adds specificity but not differentiation.

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 provides an explicit usage guideline: 'Host enforces user confirmation via the approval gate; do NOT ask the user to re-confirm before calling.' This clarifies an important workflow step. However, it does not discuss when to use this tool versus alternatives (e.g., createRawMetric), so it lacks exclusions or when-not-to-use.

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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