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Improvado gives your AI agent direct access to Harness data through an MCP server. Query deployment frequency, pipeline failure rates, DORA metrics, and cost anomalies — all in natural language. Works with Claude, ChatGPT, Cursor, and any MCP-compatible tool.
Stop clicking through Harness dashboards and exporting execution logs. Ask your AI agent for pipeline success rates, deployment frequency, failure hotspots, and build time trends — across any service, environment, or date range.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Your AI agent doesn't just read Harness data — it acts on it. Trigger pipeline runs, roll back deployments, update feature flags, and manage approval gates through natural language.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set up watches on the pipeline metrics that matter. Your AI monitors Harness continuously and flags failure rate spikes, deployment slowdowns, and cost anomalies before they escalate to incidents.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Your AI agent doesn't just read Harness data — it acts on it. Trigger pipeline runs, roll back deployments, update feature flags, and manage approval gates through natural language.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
When a release fails, engineers spend 30–60 minutes clicking through pipeline execution logs across services to identify the root cause. Harness logs are detailed but scattered. Correlating failures across dependent pipelines requires manual investigation.
Improvado extracts Harness execution logs and normalizes them into a queryable model. The MCP server lets your AI analyze failure patterns across all pipelines in one query — correlating error messages, stages, and timing to surface root cause instantly.
Deployment frequency and lead time live in Harness. Change failure rate needs incident data from PagerDuty. MTTR requires correlating both. Building a DORA dashboard means building a pipeline, and most teams skip it entirely. Engineering health remains unmeasured.
Improvado normalizes Harness pipeline data alongside incident management data into a unified model. The MCP server lets your AI calculate all four DORA metrics in one query without custom instrumentation or dashboard maintenance.
Failed pipelines that retry automatically or get stuck in loops generate unexpected cloud resource consumption. Without a consolidated view of Harness execution costs alongside cloud spend, these anomalies aren't discovered until the billing report arrives.
Improvado joins Harness execution data with cloud cost data. The MCP server lets your AI flag pipelines generating disproportionate cost relative to their output — including retry loops, long-running stages, and resource provisioning errors.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Pipeline execution history (success/failure, duration, stage-level data), deployment frequency, rollback events, feature flag states, approval gate activity, cost visibility data, and DORA metric inputs. Both Harness CI and Harness CD are supported.
Yes. The MCP server connects via Harness APIs, which are available on both Harness SaaS and self-managed installations. You provide your Harness API endpoint and credentials during setup.
Both. Read operations include all execution analytics and DORA metrics. Write operations include triggering pipeline runs, rolling back deployments, managing feature flags, and updating approval settings. Permissions are scoped to your Harness API key.
Improvado normalizes Harness deployment data alongside incident management data (PagerDuty, OpsGenie) into a unified model. Your AI can calculate all four DORA metrics in one query without building custom instrumentation — deployment frequency, lead time for changes, MTTR, and change failure rate.
Any MCP-compatible client — Claude Desktop, ChatGPT, Cursor, Windsurf, Gemini, and custom applications using MCP HTTP transport. Claude is the most commonly used due to native MCP support.
If you're already an Improvado user, connect Harness in the integrations panel and start querying at app.improvado.io/agent. For Claude Desktop or Cursor, add one line to your MCP config — under 60 seconds.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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