sentry · MCP Server

Connect Sentry to AI with Improvado MCP

Improvado's MCP server connects Sentry to Claude, Cursor, and other AI agents. Query your Sentry data in natural language — no manual exports or API scripts required.

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

Read

Read: Instant Answers from Sentry

Stop hunting through Sentry's issue list manually. Ask your AI agent for error trends, performance regressions, impacted users, and release health across every project — in one question.

Example prompts

"Show me the top 10 unresolved errors by frequency in the last 7 days, grouped by project."

20 min → 30 sec

"Which release introduced a new spike in JavaScript exceptions? Compare v2.4.1 vs v2.4.0."

45 min → 1 min

"How many unique users were affected by checkout errors this week across all environments?"

30 min → 20 sec

Works with Claude ChatGPT Cursor +5

Write

Write: Automate Sentry Actions

Triage faster. Your AI agent can assign issues, update statuses, create alert rules, and bulk-resolve noise — without you clicking through Sentry's UI.

Example prompts

"Assign all unresolved P0 issues in the payments project to the on-call engineer."

15 min → 20 sec

"Create an alert rule: notify #eng-alerts in Slack if error rate exceeds 5% for any transaction in production."

25 min → 1 min

"Bulk-resolve all issues that haven't recurred in 30 days and are marked as low priority."

1 hr → 2 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Catch Sentry Issues Before They Escalate

Set AI-powered watches on error rates, performance regressions, and release health. Get context-aware alerts that tell you what broke, which release caused it, and how many users are affected — before your customers notice.

Example prompts

"Alert me if any transaction's p95 latency increases more than 40% compared to the previous 7-day average."

Manual → auto

"Every Monday: send a summary of new issues, resolved issues, and top 5 recurring errors per project."

2 hrs → auto

"Flag any new release that causes an error rate spike above 2% within the first hour of deploy."

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

Alert Fatigue Buries Real Issues

The problem

Teams receive hundreds of Sentry notifications per day. Distinguishing a genuine P0 regression from recurring background noise requires opening multiple issues, comparing event timelines, and cross-referencing release data — a manual process that takes 30+ minutes and still misses things.

How MCP solves it

Ask your AI agent to classify and prioritize Sentry issues by impact: affected users, error frequency trend, first-seen timing relative to releases. Get a triaged shortlist, not a wall of alerts.

Try asking

Of all unresolved errors opened in the last 24 hours, which ones have the highest user impact and correlate with today's deploy?

Answer in seconds

All data sources, one query

Challenge 2

Connecting Errors to Specific Releases Is Manual

The problem

A spike appears on Monday morning. Was it the Friday deploy? The infrastructure change? The third-party SDK update? Engineers spend hours pulling release timelines, comparing error counts pre/post deploy, and reading stack traces to find the culprit.

How MCP solves it

Improvado MCP correlates Sentry error timelines with release data automatically. Ask your AI agent to diff two releases, surface new issues introduced, and quantify the regression — in seconds.

Try asking

Compare error rates and new issues between release v3.1.0 and v3.0.9. What changed?

Full detail preserved

No data loss on export

Challenge 3

Cross-Project Error Visibility Does Not Exist

The problem

Large engineering orgs run 10–30 Sentry projects. Getting a cross-project health summary means opening each project dashboard separately, taking screenshots, and stitching together a picture in a slide deck. By the time leadership sees it, it's already outdated.

How MCP solves it

Query all Sentry projects through one prompt. Your AI agent aggregates error rates, performance budgets, and release health across every project and formats an executive-ready summary in under a minute.

Try asking

Give me a health summary across all 15 Sentry projects: error rate trends, any projects with degraded performance, and the top unresolved issue per project.

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 is Sentry MCP?

Sentry MCP is a Model Context Protocol server that connects your Sentry error tracking data to AI agents like Claude, ChatGPT, and Gemini. It lets you query errors, performance issues, release health, and alerts in natural language — without logging into the Sentry dashboard or writing scripts — all through Improvado's hosted MCP server.

Which Sentry data can I access through the MCP server?

Issues, events, stack traces, performance transactions, release health, error rate trends, affected user counts, alert rules, and project metadata. Essentially everything accessible via Sentry's REST API is queryable through the AI agent.

Can the AI agent modify Sentry issues or only read data?

Both. Read operations cover querying errors, trends, and performance data. Write operations include assigning issues, updating statuses (resolved, ignored, archived), bulk-resolving issues, and creating or modifying alert rules. Permission scope is controlled by your Sentry API token.

Does this work across multiple Sentry organizations and projects?

Yes. Improvado's MCP server handles multi-project and multi-organization queries. You can ask a single question that spans all your Sentry projects simultaneously — no need to switch between dashboards or run separate queries per project.

Is my Sentry data secure through the MCP server?

Yes. Improvado stores all API tokens in an encrypted vault certified to SOC 2 Type II. Your AI agent never has direct access to credentials — requests go through Improvado's secure proxy. Prompt injection protection is built in.

How quickly can I set this up?

Under 60 seconds for Claude Desktop or Cursor users. Add the MCP server URL to your config, authenticate with a Sentry API token, and you're querying. Improvado users already connected to Sentry can start immediately via app.improvado.io/agent.

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