harness · MCP Server

Harness + Improvado MCP — Pipeline Intelligence Without the Dashboard

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.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Pull Any Pipeline Metric Instantly

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.

Example prompts

"What's the deployment success rate by service for the last 30 days? Show me the top 5 failure causes."

40 min → 30 sec

"How many production deployments did we do last week vs the prior week? Which services had the most rollbacks?"

20 min → 15 sec

"Show me DORA metrics for Q2: deployment frequency, lead time, MTTR, and change failure rate by team."

3 hrs → 2 min

Works with Claude ChatGPT Cursor +5

Write

Write: Trigger and Manage Pipelines Through Conversation

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.

Example prompts

"Roll back the payments service to the last stable version in production."

15 min → 2 min

"Trigger the full regression test pipeline for the auth service. Notify the team when it completes."

10 min → 1 min

"Enable the dark launch feature flag for the new checkout flow in staging only."

10 min → 30 sec

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Track Deployment Health Automatically

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.

Example prompts

"Alert me if change failure rate for any production service exceeds 10% in a rolling 24-hour window."

Manual → auto

"Every Monday at 9am: send a deployment digest — total deploys, success rate, MTTR, and top failure causes."

2 hrs → auto

"Flag any pipeline that has failed 3 or more times in the last 6 hours."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Failure Root Cause Analysis Across 50+ Pipelines

The problem

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.

How MCP solves it

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.

Try asking

What caused the most pipeline failures in production last week? Show error patterns by service and stage.

Answer in seconds

All data sources, one query

Challenge 2

DORA Metrics Require Custom Instrumentation

The problem

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.

How MCP solves it

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.

Try asking

Calculate DORA metrics for the last quarter: deployment frequency, lead time, MTTR, and change failure rate by team.

Full detail preserved

No data loss on export

Challenge 3

Cloud Cost Spikes from Failed Pipelines Go Unnoticed

The problem

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.

How MCP solves it

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.

Try asking

Which pipelines generated the most cloud spend last month? Flag any with high failure rates that may indicate retry loops.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What Harness data can I access through the MCP server?

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.

Does this work with Harness Cloud and self-hosted Harness?

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 — all through Improvado's hosted MCP server.

Can the AI trigger pipelines or just read data?

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.

How does this help with DORA metrics specifically?

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.

Which AI tools work with this Harness MCP server?

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 — all through Improvado's hosted MCP server.

How long does setup take?

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.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms