Can this AI integration actually mark errors as fixed?

Yes. This agent component possesses mutable write access. If you invoke the prompt properly, it will fire the `resolve_issue` tool, marking the corresponding exception ID completely dealt with inside the Sentry ecosystem. It can also erase bugs fully via `delete_issue`.

What is the difference between inspecting an 'Issue' and an 'Event'?

An 'Issue' gathers underlying multiple occurrences of the identical stack exception into one overarching master group. In contrast, querying an 'Event' (`get_event_details`) focuses the AI on a strictly singular, point-in-time incidence where the system crashed.

Do I need to supply the Organization Slug with every command?

You configure the overarching Organization Slug strictly at startup globally. For project-level filters, let your LLM query `list_projects` first to fetch internal slugs naturally into its memory buffer without constant repeated user inputs.

Sentry MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Sentry
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Sentry through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Sentry Assistant",
            instructions=(
                "You help users interact with Sentry. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Sentry"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Sentry MCP Server

Equip your favorite LLM interface with direct, real-time investigative access over your application's Sentry operational environments. Skip the grueling task of combing through the rigid crash dashboard visually. Now, your AI can pull up the latest software exceptions directly into Cursor or an MCP-enabled chat window, read the contextual stack trace natively, and even close out resolved bugs.

The OpenAI Agents SDK auto-discovers all 10 tools from Sentry through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Sentry, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Live Crash Monitoring — Query the list_issues functionality at any time to instantly see which endpoints or functions are currently malfunctioning and throwing fatal alerts
Deep Error Inspection — Feed an issue_id to the agent via get_issue_details. The LLM will devour the entire stack trace, evaluate the environmental metadata, and suggest precisely which lines of code need attention
Project & Organization Forensics — Interrogate the AI regarding internal structures (list_users, list_teams) and easily scan separate software branches or repositories (list_projects) configured in your Sentry silo
Alert Triage (Mutable) — Dictate the agent to close resolved items (resolve_issue), marking the exception safely as handled without having to load the web interface

The Sentry MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Sentry to OpenAI Agents SDK via MCP

Follow these steps to integrate the Sentry MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Sentry

Why Use OpenAI Agents SDK with the Sentry MCP Server

OpenAI Agents SDK provides unique advantages when paired with Sentry through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Sentry + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Sentry MCP Server delivers measurable value.

Automated workflows: build agents that query Sentry, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Sentry, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Sentry tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Sentry to resolve tickets, look up records, and update statuses without human intervention

Sentry MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Sentry to OpenAI Agents SDK via MCP:

delete_issue

This action is irreversible. Permanently deletes an issue

get_event_details

Retrieves details for a specific event

get_issue_details

Retrieves details for a specific issue

list_events

Lists recent events for a project

list_issues

Lists all issues (errors) in a project

list_organization_teams

Lists all teams in an organization

list_organization_users

Lists all users in an organization

list_organizations

Lists all Sentry organizations

list_projects

Lists all projects in an organization

resolve_issue

This is a reversible side-effect. Resolves an issue in Sentry

Example Prompts for Sentry in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Sentry immediately.

"Enumerate the most recently flared active open errors across the 'frontend-ui' project portal in Sentry."

"Fetch all pertinent internal parameters regarding issue id 6B3VX4921."

"I've deployed a patch fixing the deadlock in db.ts. Mutate this specific issue globally to 'resolved'."

Troubleshooting Sentry MCP Server with OpenAI Agents SDK

Common issues when connecting Sentry to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Sentry + OpenAI Agents SDK FAQ

Common questions about integrating Sentry MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.