Use case · AI agents + MCP
The bug tracker your AI coding agent can actually read
BugMojo runs an MCP server, so an AI agent like Claude or Cursor reads a bug and its full captured context — replay events, console logs, network requests — directly, instead of you pasting fragments into a chat. Assign the fix to an AGENT the same way you would a teammate.
The problem
AI agents fix code well — but they never get to see the bug
The context that explains a bug lives at runtime. Most workflows throw it away before the agent ever gets a look.
The bug loses its context on the way to the agent
By the time a report reaches an AI agent it is a title and a paragraph. The replay, the console error, the failing request — the things that actually explain the bug — got flattened into prose or dropped entirely. The agent guesses.
You become a copy-paste bridge
Reproducing the bug, exporting a HAR, pasting stack traces, describing what the user clicked — you hand-carry context from the tracker into the agent’s chat window on every single issue. It is slow, lossy, and it does not scale.
Agents cannot see live application state
An AI coding agent is good at the code, but a bug lives in runtime: the exact DOM at the moment of failure, the console warning three seconds earlier, the 500 that came back with an empty body. Without that, fixes are speculative.
There is no clean way to hand a bug to an agent
Most trackers assume every assignee is a human. There is no first-class way to say “this one is the agent’s”, track it, and keep it separate from your team’s seats and workload.
How BugMojo helps
Give the agent the whole bug — automatically
Each pain maps to a real capability. Capture once in the browser; the agent reads the same context you do, over MCP.
An MCP server the agent connects to directly
BugMojo ships a Model Context Protocol server. Point Claude, Cursor, or any MCP-capable agent at it and the agent can read a bug and its full captured context on its own — no copy-paste bridge, no re-describing the repro.
The full capture, not a summary
One click in the browser extension records an rrweb DOM session replay, console logs, network requests, and a screenshot. The agent reads the same artifacts you do — replay events, console output, the failing request — as structured context.
Assign the fix to an AGENT
Polymorphic assignees mean an assignee is a MEMBER or an AGENT. Hand a bug to an agent the same way you would to a teammate, track it as its own work item, and keep human and agent ownership cleanly separated.
PII redacted before it ever leaves the browser
Redaction runs client-side in the extension. Input values and sensitive fields are masked during recording, so what the agent reads through MCP was already minimized before anything left the page.
Quick Capture — zero project setup
Capture a bug with full context without configuring a project first. Get an agent-readable report from the very first capture, then organize into projects when you are ready.
Everything reachable over one API
BugMojo is AI-native: every bug and its context is available through the same endpoints your UI uses. The MCP server exposes that surface to agents, so what a human can open, an agent can read.
The workflow
From one-click capture to an agent-drafted fix
Capture the bug in one click
A teammate or tester hits the extension. It records the rrweb replay, console logs, network requests, and a screenshot, redacts PII in the browser, and files the bug — no project setup required with Quick Capture.
Assign it to your agent
Set the assignee type to AGENT. The bug is now owned by your AI coding agent and tracked as its own work item, separate from your team’s seats.
The agent reads the full context over MCP
Connected through the MCP server, the agent pulls the bug and its captured context directly — replay events, console output, the failing network request — into its working context. No pasting, no re-describing.
It proposes the fix where you code
With real runtime evidence instead of a guess, the agent reasons about the actual failure and drafts a fix in your editor. You review, refine, and ship.
Why it works
Built to be read by humans and agents alike
Structured context, not a screenshot dump
Replay events, console logs, and network requests are captured as structured data the agent can parse and reason over — not an image it has to squint at.
Agents are teammates, not seats
The polymorphic assignee model treats a MEMBER and an AGENT as first-class owners. Assign, track, and report on agent-owned bugs — and AI agents never consume human seats.
Safe context by construction
Because PII redaction runs client-side before capture leaves the browser, the context an agent reads over MCP was already minimized at the source.
Start in seconds with Quick Capture
No project to configure before your first capture. Get an agent-readable bug from the very first click, then organize into projects when it suits you.
FAQ
Questions about MCP and AI agents
- What is MCP and why does it matter for bug fixing?
- MCP (Model Context Protocol) is an open standard that lets AI agents read from external tools in a structured way. BugMojo runs an MCP server so an agent can pull a bug’s full captured context — replay, console, network — directly, instead of you pasting fragments into a chat.
- Which AI agents work with BugMojo?
- Any MCP-capable coding agent, including Claude and Cursor. If your agent speaks the Model Context Protocol, you connect it to BugMojo’s MCP server and it can read bugs and their context.
- What exactly can the agent see?
- The same artifacts a human sees on the bug: the rrweb DOM session replay events, the console logs, the network requests, and the screenshot. PII is redacted client-side during capture, so the agent reads context that was already minimized in the browser.
- Which plan includes MCP-powered AI agents?
- MCP is free on every plan, including Free — connect Claude Code, Cursor, or Windsurf and let them read captures without paying anything. AI agents never consume human seats. Pro ($12 per user / month) adds longer retention and more seats; Team ($24 per user / month) adds SSO and automatic agent dispatch.