groundcover MCP server

Unlimited context to your AI agent

groundcover MCP delivers production-grade telemetry to LLMs, IDEs, and on-call workflows, with zero code and zero noise.

Enrich your AI workflows with real observability context

AI agents often fall short when querying traditional APIs for system insights. groundcover MCP Server changes that — connecting groundcover's deep observability data to tools like Cursor and Claude Code. Your favorite AI assistant can now access real-time production data to power smarter debugging, incident troubleshooting and automation, natively and effortlessly.

Accelerate incident
response with AI

groundcover MCP Server enables your AI assistants to connect the dots across logs, metrics, traces, and infrastructure metadata to spot anomalies, detect subtle trends, and explain the “why” behind incidents. It's not just an assistant—it's an always-on observability expert embedded in your workflow. Turn your AI agent into a senior SRE on your team 24/7 — only faster, scalable, and embedded directly into your workflow.

Natural language meets
deep telemetry

Observability data isn’t a single stream - it’s a dense mesh of logs, traces, metrics, and infrastructure metadata. Traditionally, making sense of it meant mastering query languages, juggling dashboards, and stitching context across different visualizations and timelines. With groundcover MCP Server, your AI agents surface rich, correlated answers from the full observability stack — using a natural language query prompt. No query syntax. No tab fatigue. Just instant, unified insight.

Real-life use cases

Explore how engineering teams are using groundcover MCP to accelerate their work, troubleshoot faster, and build with confidence, powered by AI-driven observability.

  • Support agent

    Instantly answer customer inquiries with context-aware insights pulled from real-time logs, metrics, and traces. groundcover MCP helps support teams quickly identify root causes and respond with clarity.

  • On-call agent

    When every second counts, MCP acts as your co-pilot. Reduce mean time to resolution (MTTR) with instant anomaly detection, AI-generated summaries of system behavior, and guided investigation paths across your environment.

  • Dev IDE agent

    Get observability baked into your development workflow. MCP provides live feedback on service health, performance regressions, and error traces, right when and where developers need it most, even before code hits staging.

Tooling access to observability

Expose core observability features through reliable, fast APIs:

query_logs
Query logs from groundcover.
Usage
Based on the use case, use patterns (for an aggregated view) or records (for raw lines).
For patterns, use a broad time window and limit the logs number.
For records, use a tight time window and limit the logs number.
Filter fields
envs, clusters, namespaces, workloads, pods, containers, levels, and any Attributes key=value (* wildcards).
Important Notes:
Returned timestamps are in the timezone of the request (the user)
Parameters:
attributes (object): Attributes to filter logs by. Format: key=value. Supports wildcards using, e.g. key=vaue
clusters (array): The clusters to filter the logs by
containers (array): The containers to filter the logs by
end (string): The end time in RFC3339 format, default is now
envs (array): The environments to filter the logs by
levels (array): The levels to filter the logs by, possible values: critical, fatal, error, warning, info, debug, trace, unknown
limit (integer): The limit of the number of items to return
mode (string): The mode to use for the query (enum: records, patterns)
namespaces (array): The namespaces to filter the logs by
patternId (string): The pattern id to filter the logs by
period (string): The period to filter by in ISO 8601 duration format, default is PT1H
pods (array): The pods to filter the logs by
skip (integer): The number of items to skip for pagination
sortOrder (string): The order to sort by, default is desc (enum: asc, desc)
start (string): The start time in RFC3339 format, default is 1 hour ago
workloads (array): The workloads to filter the logs by

Observability
for what comes next.

Start in minutes. No migrations. No data leaving your infrastructure. No surprises on the bill.