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Glama
kowshik3383

Production Monitoring MCP

by kowshik3383

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": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_observability_statusA

Inspect the status of connected production monitoring providers (Sentry, GitHub, Vercel, Better Stack, Cloudflare) and list any missing credentials.

get_production_healthA

Single-call operational pulse across all connected services (Uptime, Sentry error spikes, latest Vercel deploy, Cloudflare 5xx). Returns lean status signals rather than verbose raw logs.

get_recent_errorsA

Fetch recent production errors from Sentry for a project or service, including frequency counts, first/last seen timestamps, and affected user counts.

get_error_detailsA

Fetch deep technical details for a specific Sentry issue including parsed in-app stack trace frames, code context, tags, and breadcrumbs.

find_regressionC

Find errors in Sentry that have regressed or were first introduced in a specific release or timeframe.

get_deploymentsB

Fetch recent deployments from Vercel (or GitHub) with deployment state, commit SHA, branch, and timestamp.

get_deployment_logsA

Fetch runtime and build logs for a specific Vercel deployment to diagnose deployment failures.

compare_deploymentsA

Compare two deployments or commit SHAs via GitHub to see all commits, authors, and exact files changed.

get_commit_detailsB

Inspect a specific GitHub commit's message, author, stats, and modified files with diff snippets.

check_uptimeB

Query Better Stack Uptime monitors to check availability, status (up/down), and recent downtime incidents.

analyze_logsB

Query structured application logs from Better Stack Logs to investigate log anomalies, errors, and traces.

analyze_api_latencyC

Analyze edge HTTP traffic, status codes (2xx, 4xx, 5xx), and error rates from Cloudflare Analytics.

correlate_incidentC

Run automated triage correlating deployment timestamps, git diffs, and Sentry stacktraces to find the root cause.

explain_incidentB

Generates a human-readable executive briefing of an incident with root cause, evidence chain, confidence, and recommended action.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.6/5.0

Scored across 14 tools

Disambiguation4/5

Most tools target distinct resource+action pairs (Sentry errors vs deployments vs uptime vs latency), and descriptions clarify boundaries. Slight overlap between get_production_health and get_observability_status (a pulse vs provider/credential status), and correlate_incident/explain_incident form a related triage/briefing pair, but each remains differentiable.

Naming Consistency5/5

All 14 tools use snake_case with a consistent verb_noun pattern (get_recent_errors, check_uptime, analyze_logs, compare_deployments, explain_incident). No mixing of camelCase or vague bare verbs.

Tool Count5/5

14 tools is well within the ideal 3-15 range and each earns its place across distinct monitoring and incident-response stages. No redundant or filler tools.

Completeness5/5

The surface covers the full incident lifecycle: detection (health, errors, uptime, latency), investigation (error details, deployment logs, commit details, regressions), correlation (correlate_incident), and reporting (explain_incident). No obvious dead ends for the stated production monitoring purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues