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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
MENDRIFT_DEMONoSet to 1 for demo mode using fixture data, or 0 for live mode against real infrastructure.1

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
get_drift_reportA

Compute a data/prediction drift report for a deployed model.

Compares the current serving window against a reference window (PSI + KS
tests per feature). Returns per-feature drift scores, an overall drift
flag, and the top drifted features.

Args:
    model_name: Registered model name as it appears in the model registry.
    reference_window_hours: Reference window size (default 7 days).
    current_window_hours: Current window size (default 24 hours).
summarize_metric_anomaliesA

Summarize serving-metric anomalies (latency, error rate, prediction stats).

Returns metric statistics with anomaly windows flagged via rolling z-score, suitable for an LLM to reason over without raw time series.

get_deployment_historyA

List recent deployments/version transitions for a model, newest first.

diff_deploymentsA

Diff two model versions: training data span, params, eval metrics, feature schema.

The primary root-cause tool: correlates 'what changed' between the incumbent
and the newly deployed version.
propose_rollbackA

Generate a rollback plan for human review. Read-only — does not execute.

execute_rollbackA

Execute an approved rollback. Rejects missing/invalid tokens.

open_incidentB

Open an incident record (demo: JSONL log; live: ticketing webhook).

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.9/5.0

Scored across 7 tools

Disambiguation5/5

Each tool targets a distinct action and resource: drift reports, metric anomalies, deployment history, deployment diffs, incidents, and rollback planning/execution. There is no ambiguity between tools like propose_rollback and execute_rollback because one is read-only planning and the other is execution.

Naming Consistency5/5

All tools use lowercase snake_case with a verb_noun pattern (propose_rollback, execute_rollback, open_incident, get_drift_report, summarize_metric_anomalies, get_deployment_history, diff_deployments). The naming is uniform and predictable, making it easy to infer tool behavior from the name.

Tool Count5/5

Seven tools is a well-scoped count for a drift monitoring and mitigation server. Each tool covers a distinct part of the workflow—detection, investigation, incident management, and rollback—without unnecessary redundancy or overwhelming the agent.

Completeness4/5

The core lifecycle is covered: drift detection, anomaly summarization, deployment history/diffing, incident opening, and rollback proposal/execution. The main gap is that incidents can be opened but not listed, closed, or updated, and there is no rollback status tracking. These are workable gaps but slightly incomplete for a full incident management loop.

Maintenance

ActivitySlowing
ResponsivenessNo issues