Sentry connector for AI agents

CALL ANY TOOL

List and inspect issues, pull stack traces and events, and resolve or assign errors, each call scoped to the authenticated engineer's project access.

sentry_issues_list

List issues

Fetch a paginated list of issues for a project, filtered by status, level, or time range.

Parameters

Name

Type

Required

Description

project_slug

string

Required

Project to list issues from

query

string

Optional

Sentry search query, e.g. is:unresolved

stats_period

string

Optional

Time window such as 24h or 7d

sentry_issue_get

Get issue

sentry_events_list

List events

sentry_issue_update

Update issue

sentry_projects_list

List projects

sentry_release_get

Get release

Build your Agent

The same per-user auth pattern wires into every agent framework.

import { ScalekitClient } from "@scalekit-sdk/node";
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(process.env.SCALEKIT_ENV_URL, process.env.SCALEKIT_CLIENT_ID, process.env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["sentrymcp"], toolNames: ["sentry_issues_list", "sentry_issue_get", "sentry_events_list"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm: new ChatOpenAI({ model: "gpt-4o" }), tools });
await agent.invoke({ messages: [{ role: "user", content: "Triage unresolved payments errors" }] });

import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const openai = new OpenAI();
const sk = new ScalekitClient(process.env.SCALEKIT_ENV_URL, process.env.SCALEKIT_CLIENT_ID, process.env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["sentrymcp"], toolNames: ["sentry_issues_list", "sentry_issue_get", "sentry_events_list"] },
  pageSize: 100,
});

const res = await openai.responses.create({
  model: "gpt-4o",
  input: "Show top unresolved errors in payments",
  tools,
});

import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const anthropic = new Anthropic();
const sk = new ScalekitClient(process.env.SCALEKIT_ENV_URL, process.env.SCALEKIT_CLIENT_ID, process.env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["sentrymcp"], toolNames: ["sentry_issues_list", "sentry_issue_get", "sentry_events_list"] },
  pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  tools,
  messages: [{ role: "user", content: "Resolve the checkout NullPointer issue" }],
});

import { LlmAgent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(process.env.SCALEKIT_ENV_URL, process.env.SCALEKIT_CLIENT_ID, process.env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["sentrymcp"], toolNames: ["sentry_issues_list", "sentry_issue_get", "sentry_events_list"] },
  pageSize: 100,
});

const agent = new LlmAgent({ name: "sentry_agent", model: "gemini-2.0-flash", tools });
await agent.run("List new issues in the latest release");

Try these prompts

Paste any prompt into your agent to start working Sentry issues.

Triage & resolve

Copy the prompt

Copied

What are the top unresolved errors in the payments service this week?

Copy the prompt

Copied

Resolve the checkout NullPointer issue and assign it to me.

Copy the prompt

Copied

Ignore the noisy rate-limit 429 issue in the api project.

Investigate

Copy the prompt

Copied

Pull the latest stack trace for issue PROJ-1421.

Copy the prompt

Copied

Show me events for the TimeoutError over the last 24 hours.

Copy the prompt

Copied

Which release introduced the checkout regression?

Report & recall

Copy the prompt

Copied

List all projects I have access to.

Copy the prompt

Copied

Summarise new issues since the last deploy.

Copy the prompt

Copied

How many events did the payments timeout error get this week?

SEE HOW AUTH WORKS

The engineer authorises Sentry once. Every later agent call uses their token with project scope enforced.

1

Authorize

Your user connects

Sentry MCP

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

2

Store

Their

Sentry MCP

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

3

Resolve

When your agent calls a

Sentry MCP

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

4

Audit

Every

Sentry MCP

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

See the same per-user auth pattern across other monitoring connectors.

No items found.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Monitoring data needs real per-user attribution.

01.

Shared tokens break per-user analytics

A shared Sentry token looks fine in a demo. In production every resolve and assign logs as one service account, so you lose track of who acted. Scalekit resolves the real engineer's credential per call.

// shared token
resolve → monitoring_bot

// scalekit
resolve → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Sentry today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Sentry as the user or as a shared key?

As the user. Scalekit resolves the authenticated engineer's own Sentry credential at request time, so every issue read, resolve, and assignment is attributed to them in Sentry's audit log rather than a shared monitoring bot.

Where is the Sentry token stored?

In an AES-256 encrypted vault, namespaced per tenant. Tokens are fetched only at the moment of a tool call, refreshed automatically before expiry, and revocable from a single dashboard, never exposed to the model or the prompt.

Can I limit what the agent is allowed to do in Sentry?

Yes. Pass a toolNames filter to listScopedTools to expose only the actions you want, for example read-only issue listing without resolve or delete. Scalekit also runs a scope check before each API call.

What happens when a user revokes Sentry access?

The credential is invalidated at the next tool call, which fails closed rather than falling back to a shared token. Other users are unaffected, and the revocation is recorded in the audit trail.

Can the agent modify production issues, or only read them?

That is your choice. Expose only sentry_issues_list and sentry_issue_get for a read-only triage agent, or add sentry_issue_update to let it resolve and assign. The write scope is enforced per user, so an engineer can only change issues in projects they already own.

Sentry MCP

Tools your monitoring agent reaches for on Sentry, scoped per user.