Imagine this: a crash report lands in your tracking system, and instead of combing through logs, stack traces, and device details, you simply ask your AI-powered IDE to fix it. Within moments, the agent retrieves all the necessary context, analyzes the codebase, and proposes a patch—all without ever exposing your local environment or sensitive data to third parties.
That’s exactly what Bugsee MCP Server enables.
What Is Bugsee MCP Server?
Bugsee MCP Server is our implementation of the Model Context Protocol (MCP), a new standard designed to connect AI agents with external data sources and developer tools in a secure, structured way.
With Bugsee’s MCP server, your AI assistant—whether it’s integrated into Cursor, Claude, Windsurf, or any other MCP-compatible IDE—can fetch rich, context-heavy bug reports directly from Bugsee. This includes the detailed crash data that has always made Bugsee unique: stack traces, device logs, lifecycle events, and other runtime breadcrumbs that replicate the exact conditions leading to the issue.
How It Works
Tools Exposed
At launch, Bugsee MCP Server provides a simple but powerful tool:get_issue — retrieve detailed issue data by key (e.g., "MYAPP-123")
This allows your AI agent to instantly pull all relevant crash context from Bugsee when you reference an issue.
Prompt-Based Invocation
Some agents support special commands or prompt styles to make this even easier. For example, you could type:
“Help analyze and fix IOS-1443 from Bugsee”
Your IDE agent will then:
- Fetch the issue from Bugsee via MCP.
- Review the crash stack, logs, and device/environment info.
- Correlate that with your local codebase and configuration.
- Propose a fix—or even make the actual code change—without leaving your environment.
Example Flow
- A crash is reported in your iOS app (Bugsee issue: IOS-1443).
- In your AI-IDE, you ask: “Fix IOS-1443 from Bugsee.”
- The agent uses Bugsee MCP’s get_issue tool to retrieve the full crash report.
- Armed with Bugsee’s unique context, the agent navigates your local repo, identifies the bug, and suggests or applies the fix.
- Crucially, the agent can then validate its changes in your environment by:
- Running your linter to ensure style and syntax consistency.
- Executing your unit and integration tests to confirm functionality.
- Checking against your local CI/CD setup to make sure the fix won’t break your build or deployment pipeline.
This makes the debugging workflow not just automatic, but also trustworthy and production-ready.
Privacy & Control — Your Secret Weapon
What makes Bugsee MCP stand out isn’t just the data richness—it’s how that data is used.
No sensitive data leaves your environment: Only Bugsee’s crash context is shared with the AI agent.
Local autonomy: The agent has direct access to your code and configuration, but Bugsee does not.
Compliance & trust: This separation ensures developer privacy, keeps client data protected, and respects legal boundaries.
Bugsee MCP makes your AI agent smarter without exposing your secrets.
Similarly, Bugsee’s AI Bug Reporting Insights feature lets you analyze crashes securely inside the Bugsee dashboard, keeping your code and sensitive data private.
Final Thoughts
With Bugsee MCP Server, debugging becomes collaborative: Bugsee supplies the context, your AI assistant does the heavy lifting, and you stay in full control.
And because the agent can leverage your linters, tests, and CI/CD pipeline, the fixes it proposes aren’t just quick—they’re reliable, consistent, and aligned with your team’s standards.
This is the future of software debugging—context-rich, agent-friendly, and privacy-preserving.