How groundcover Powers MCP with Digestible Observability Data

Large Language Models (LLMs) are brilliant at drawing insights, but only when they can actually see the data. In observability, that data lives in torrents of logs, traces, and metrics. Feeding such fire‑hoses of information to an AI assistant is messy, slow, and expensive.

The Model Context Protocol (MCP) changes that. It standardises how an AI assistant retrieves exactly the context it needs, no matter which vendor’s LLM you use or where the data lives. In this post, we’ll show how MCP works, why it matters for monitoring, and how groundcover’s new MCP server turns raw observability streams into AI‑ready answers.

MCP in 60 seconds

• What it is – An open, client–server protocol (introduced by Anthropic in late 2024) that lets an LLM call external tools or fetch data through a single, predictable interface.
• Why it exists – Without MCP, you write N × M bespoke integrations (every LLM ↔ every data source). With MCP, you write one connector per system, and any MCP‑aware LLM can use it.
• How it works – The LLM (client) sends a standardised request, an MCP server – often maintained by the data/tool owner – executes the action, enforces permissions, and returns a tightly scoped result.
• Key benefits – Plug and play integrations, model‑agnostic flexibility, and secure, on‑demand context delivery.

The observability context challenge for LLMs

Now, let’s zero in on the world of observability, an area that stands to benefit immensely from LLM integration, if we can solve the context problem. Observability data (logs, metrics, traces, anomaly alerts, etc.) is what DevOps and SRE teams use to understand system behavior. It’s rich with insights about errors, performance bottlenecks, security incidents, and more. The idea of an LLM that can digest this data and act as an intelligent co-pilot for on-call engineers is exciting: imagine asking in plain English, “Why is the checkout service erroring out?” and getting a useful answer pinpointing the likely cause.

However, anyone who’s dealt with real-world observability knows the volume and complexity of this data can be overwhelming, both for humans and AI. Here are some of the challenges of delivering observability context to an LLM:

• Sheer volume: Modern cloud-native environments generate billions of log lines and countless metrics each day. No LLM (even those with 100k token contexts) can simply ingest 100,000 raw log lines and make sense of it in one go. The context window and memory limitations force us to be selective.‍
• Noise vs. signal: Observability data is noisy by nature. Logs are full of repetitive entries, routine status messages, and variable fields (timestamps, request IDs, IPs) that don’t carry meaning. Without preprocessing, an LLM would waste effort trying to find patterns in this noise.‍
• Cost and latency: Sending large volumes of raw data to an LLM, especially over an API, would be slow and expensive. LLM APIs charge by token usage, so blindly feeding thousands of lines incurs real cost.
  

In short, naively trying to shove unfiltered observability data into an AI is inefficient. The smarter approach, and indeed the one enabled by MCP, is to deliver context in a distilled, structured form.

groundcover’s MCP approach: LLM‑tailored tools for logs, traces, and anomalies

groundcover has been rethinking how to fuse AI into observability. Rather than simply exposing the same old logging API to an LLM, groundcover is building dedicated, simplified tools - essentially an MCP server - specifically tuned for LLM consumption of observability data. The motivation is straightforward: make it easy for an AI to grab insights, not just raw data.

So, what makes groundcover’s MCP server different from a generic log API? It’s not just one thing — it’s the combination of purpose-built design choices that make it LLM-native. Summarization is part of it, but it’s also about how we structure, prioritize, and expose data in a way that fits how models think. We’re not just handing off raw inputs — we’re actively guiding the agent’s next steps, just like we do in our UI for users. The goal is the same: surface the most relevant signals, highlight what stands out, and shape the flow of investigation so the next question is obvious — and easy to answer.

Log Patterns - summarizing repetitive logs: groundcover’s Log Patterns feature automatically identifies repetitive log messages and abstracts out the variable parts. Rather than showing an AI 5,000 instances of “Error connecting to DB at <IP>…”, groundcover can provide a pattern like “Error connecting to DB <IP4>” with a count of occurrences. The result is a streamlined view of log structures, instead of millions of raw, cluttered log lines. For an LLM, this is a dream: the input is compact and focused on the general shape of events.

Drilldown mode - focusing on key attributes: groundcover’s Drilldown mode acts as an intelligent lens on trace or log data. It analyzes a set of traces or logs, and highlights the most informative attributes - in other words, the fields that stand out statistically and could point to anomalies or bottlenecks. By surfacing these high-impact attributes first, Drilldown mode spares the AI from wading through all trace details. The LLM can get a concise summary like, “In 80% of error traces, customer_type=premium and region=us-west stand out”, which directs it to a plausible culprit.

Anomaly insights - highlighting what matters: groundcover is also injecting anomaly detection into the context pipeline. Our Insights feature automatically detects unusual spikes or surges in log volume. Rather than the LLM having to guess where it begin an investigation, the MCP server can directly inform it: “There was a 5× spike in error logs for service checkout at 3:45 PM, compared to baseline.”

All these features act as data summarizers that dramatically optimize context injection into LLMs. Instead of the AI receiving a raw dump of 100k log lines, it might receive a dozen log patterns with counts, a summary of an anomaly, and a short list of unusual trace attributes.

Why groundcover’s MCP Server Redefines Context Delivery

groundcover’s implementation of MCP shifts the focus from simply exposing observability data to preparing context the way an LLM actually needs it. Instead of overwhelming models with raw logs or verbose traces, our MCP server delivers distilled, structured insights that align with how AI systems process and reason.

Take an on-call engineer investigating a production alert. Rather than parsing thousands of lines, the LLM gets a few key patterns, a spike summary, and the high-signal trace fields. It's immediately actionable. Or consider a developer debugging failing tests in staging. By tagging logs with test IDs, the agent can quickly surface the exact logs tied to failing cases - no manual digging.

Support teams can do the same. Drop in an error ID and get back a clear view of what went wrong, when it started, and which workload was involved. No escalation needed, just answers.

What sets this approach apart is that it doesn’t treat the LLM like a generic client. It treats it like a reasoning engine that benefits from curated, minimal, high-value input - and the MCP server is built to deliver exactly that.

eBPF further enhances groundcover’s MCP with more context

AI is only as effective as the data that feeds it, and this is exactly where groundcover’s unique architecture shines. Unlike traditional observability platforms, groundcover’s combination of a powerful eBPF sensor with a unique Bring Your Own Cloud (BYOC) architecture enables the automatic collection of deep infrastructure and application telemetry, which our users can stream and store with minimal overhead and no ingestion or retention costs. This unique approach of combining BYOC with eBPF ensures that customers and their agentic AI solutions have zero blindspots and maximum relevant context.

A new era of AI-driven observability

With MCP providing the interface and groundcover shaping the data, AI is no longer a bolt-on layer in observability - it’s integrated from the ground up.

LLMs can now answer questions like “Why did latency spike in checkout?” and actually produce useful answers: error patterns, trace summaries, anomaly deltas. They can follow threads of inquiry, from deployment to impact, without ever leaving the bounds of your infra.

Developers are using agents to run tests, deploy code, monitor logs and traces, and validate fixes - in one loop. Support teams use MCP-powered agents to go from error to root cause without handing it off. Engineering teams kick off investigations directly from alerts, backed by context that’s already filtered and prioritized.

This isn’t a dashboard with a chatbox. It’s AI that understands how your systems behave - because we built the interface, tools, and summaries to make that possible.