Introducing Opriven for Coding Agents
A single MCP for the runtime context your coding agent is missing.
Your coding agent can write code, generate tests, plan architecture, and deploy. But the moment something breaks in production, it can't help. You're back to context-switching between Datadog, CloudWatch, and PagerDuty, piecing things together yourself. The agent has no context on what's happening in production.
Today, we're launching the Opriven MCP. A single connection that gives your coding agent runtime context across your entire infrastructure and observability stack, enabling you to go from issue to fix without leaving your editor or CLI.
#Introducing the Opriven MCP
Opriven pulls from 50+ integrations—Datadog, CloudWatch, Grafana, PagerDuty, and more—so you don't have to manage a separate MCP for each tool.
But we don't just aggregate your data. We've built our own representation on top of those sources that maps the topology, evolution, causal behaviors, and semantic labels of your system.
This matters because individual MCPs weren't designed for this multi-faceted workflow, where you're trying to understand and diagnose a large system holistically. Each tool returns a different payload format, each requires its own tool call, and the agent has to reconstruct the relationships between these signals every time. And the more tools you load into context, the worse the output.
Opriven normalizes all of that into a small set of tools that give your agent a structured understanding of your system, not just raw signals. The result is better retrieval, faster investigations, and more accurate fixes.
Automated workflows through skills
With a simple slash command, you can leverage two powerful workflows using our MCP:
/investigate: entry point for any production problem. Describe what's going wrong, and Opriven investigates in the background./fix: get a fix applied directly to your codebase.
6 tools
| Tool | What it does |
|---|---|
investigate_issue | Trigger an async investigation. Describe symptoms, Opriven investigates using your runtime data |
get_issue_report | Pull results once ready: root cause analysis, causal graph, timeline |
get_issue_fixes | Get remediation actions: code changes, CLI commands, instructions |
search_issues | Find issues across your environments |
get_artifact | Fetch supporting evidence: metrics, traces, logs, events, files, service topology |
ask | Natural language query. Delegates to Opriven's AI agent, not a raw model call. Backed by tools and your observability data |
#An example: debugging a latency spike
- Datadog alerts fire: p99 latency on
/api/checkouthas tripled in the last ten minutes. Opriven picks up the alert and automatically starts an investigation. - You type:
/investigate the latency spikes on the checkout service. Opriven already has results. - The investigation pulled metrics from Datadog and traces from CloudWatch, built a causal graph, and traced the spike back to a feature flag rollout earlier that morning. Opriven recommends disabling the flag for immediate mitigation, and surfaces a comprehensive fix to the underlying code path it exposed.
- You type
/fix. Your agent disables the feature flag and puts up a PR with the comprehensive fix. - Latency drops back to baseline, and Opriven automatically assigns the appropriate reviewers to your comprehensive fix PR.
#Get started
The server is live at mcp.opriven.com and works in Cursor, Claude Code, VS Code with GitHub Copilot, and any editor that supports the Model Context Protocol.
If you're an Opriven customer, install the plugin from github.com/opriven/skills and bring it to your coding agent today. If you're not, book a demo and we'll get you set up.
You can find additional info at docs.opriven.com/connect.
