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Glama
unprimathe

zigmo

by unprimathe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

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
}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_metricsA

List every metric and dimension available, with its business meaning.

describe_metricB

Explain how a metric is defined (formula, filter, format).

query_metricsA

Compute metrics, optionally split by dimensions.

Args:
    metrics: metric names from list_metrics, e.g. ["gross_revenue", "orders"].
    dimensions: up to 3 dimension names, e.g. ["country"] or ["month"].
    filters: dimension -> allowed values, e.g. {"country": ["France", "Germany"]}.
    date_from / date_to: inclusive ISO dates (YYYY-MM-DD) on the invoice date.
    order_by: a requested metric or dimension. Defaults to the first metric.
    descending: sort direction.
    limit: max rows (1-1000).
    with_share: add <metric>_share = share of the total across the returned groups.
explain_queryA

Return the optimized SQL and DuckDB plan for a request, without running it.

Same arguments as query_metrics. Useful to paste the query into a BI dashboard.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 4 tools

Disambiguation4/5

The four tools target distinct stages: discovery (list_metrics), definition (describe_metric), execution (query_metrics), and dry-run inspection (explain_query). list_metrics vs describe_metric and query_metrics vs explain_query share the same domain and arguments, so there is mild potential for confusion, but the descriptions clearly separate 'list all', 'explain one', 'run', and 'don't run'.

Naming Consistency5/5

All four names follow a clean verb_noun snake_case pattern (list_metrics, describe_metric, query_metrics, explain_query). The only deviation is describe_metric using the singular 'metric' while others are plural, which is a natural, readable choice rather than an inconsistency.

Tool Count4/5

Four tools is a tight, well-scoped surface for a metrics/semantic-layer server: discover, describe, query, and preview. It is slightly lean, with no separate tool for browsing dimensions or filters, but every tool earns its place and nothing is redundant.

Completeness4/5

The read lifecycle is fully covered—discovery, definition, querying with dimensions/filters/date ranges, and an explain/dry-run path that complements execution. Minor gaps exist (no standalone dimension listing and no validation-only or saved-query operations), but agents can work around these via list_metrics and explain_query.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources