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FelipeRego

dbtBigQuery

by FelipeRego

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
DBT_PROFILES_DIRYesAbsolute path to the directory containing dbt profiles.yml, typically the project root (e.g. /absolute/path/to/dbtBigQuery).
DBT_BIGQUERY_PROJECTYesYour Google Cloud project ID where BigQuery datasets and the semantic layer reside.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_metricsA
List every metric the semantic layer defines.

Args:
    search: Optional case-insensitive substring to filter metric names,
        labels and descriptions. Leave empty to list all.

Returns the metric name, label, type, one-line description and the short
definition. Use `describe_metric` for the full governance block.
describe_metricA
Return the full governed definition of one metric.

Args:
    metric: The exact metric name, as returned by `list_metrics`.

Returns the definition, the assumption it rests on, the trade-off accepted
when it was chosen, what breaks if someone defines it differently, its
grain, and the measures or metrics it is computed from. This is the
authoritative text — it is read from the compiled dbt manifest, which is
generated from models/semantic/metrics.yml.
list_semantic_modelsA

List the semantic models — the tables the metrics are built on, with their entities, groupable dimensions and aggregatable measures.

Use this to understand why two metrics can or cannot be sliced the same way.

list_dimensionsA
List the dimensions a given set of metrics can legitimately be grouped by.

Args:
    metrics: One or more metric names, comma-separated. When several are
        given, only dimensions valid for *all* of them are returned —
        which is the honest answer, because MetricFlow will refuse a
        group-by that has no join path to one of the metrics.

Dimension names are returned in MetricFlow's `entity__dimension` form,
which is exactly what `query_metrics` expects in `group_by`.
query_metricsA
Run a metric query through MetricFlow and return the rows, together with the
governed definition of every metric involved.

Args:
    metrics: Metric names, comma-separated. Must already exist in the layer.
    group_by: Dimensions to group by, comma-separated, in MetricFlow's
        `entity__dimension` form (e.g. `user__acquisition_medium`).
        Use `metric_time` for the metric's own time axis, optionally with a
        grain suffix such as `metric_time__week`.
    start_time: Inclusive ISO date lower bound on metric_time, e.g. 2021-01-01.
    end_time: Inclusive ISO date upper bound on metric_time.
    where: A MetricFlow filter expression, e.g.
        "{{ Dimension('user__first_device_category') }} = 'mobile'".
        Dimensions must be referenced through the Dimension() wrapper; raw
        column names are rejected by the layer, not by this server.
    order_by: Fields to sort by, comma-separated. Prefix with `-` for
        descending, e.g. `-metric_time`.
    limit: Maximum rows to return. Capped at 500.

The response always includes a `governance` block. Report it alongside the
numbers rather than presenting the figures bare.
explain_metric_sqlA
Return the SQL MetricFlow would run for a query, without running it.

Args:
    metrics: Metric names, comma-separated.
    group_by: Dimensions to group by, comma-separated.
    start_time: Inclusive ISO date lower bound on metric_time.
    end_time: Inclusive ISO date upper bound on metric_time.
    where: A MetricFlow filter expression.

Useful for showing a person how a metric is actually computed, and for
checking a query's cost before spending warehouse credits on it.
health_checkA
Report whether the semantic layer is ready to serve queries: manifests
present, MetricFlow available, warehouse credentials configured.

Call this first if a query fails for reasons that look environmental.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.5/5.0

Scored across 7 tools

Disambiguation5/5

Each tool targets a distinct concern: listing metrics, listing semantic models, describing a single metric, listing valid dimensions, running queries, explaining SQL, and checking health. There is no overlap or ambiguity—an agent can clearly choose the right tool for the task.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (list_*, describe_metric, query_metrics, explain_metric_sql, health_check). No mixed conventions or vague verbs; the naming is predictable and self-documenting.

Tool Count5/5

Seven tools is well-scoped for a semantic layer server. Each tool covers a necessary aspect of exploring, querying, and validating metrics without redundancy or bloat. The count feels right for the domain.

Completeness5/5

The surface covers the full read-only lifecycle of the semantic layer: discovery (list_metrics, list_semantic_models), definition (describe_metric), grouping validation (list_dimensions), execution (query_metrics), cost/explanation (explain_metric_sql), and environment readiness (health_check). No obvious gaps for the stated purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues