A coding agent with your whole repository open can still hand you a fix that sounds right and is wrong. Firecrawl’s September 6, 2026 post frames this as a context problem, not a model problem: you asked about what happened on a server and gave it only the files on your laptop.
Three gaps the repo cannot close
Firecrawl names three kinds of missing information, each producing a different failure. The agent cannot see what happened on the server — which release was live, which users hit the error, how often it fired. It cannot see whether the bug is even yours; sometimes working code breaks because a library underneath it changed, and that discussion lives in someone else’s GitHub issues. And it cannot see whether its own fix worked, because from inside the diff a plausible edit looks like a correct one.
That third gap is the one that quietly costs the most review time. An agent that never runs the app reports success either way.
Wiring the evidence sources
Firecrawl maps each gap to a tool. Sentry answers what broke, on which line, for how many users, and connects through a hosted MCP server at mcp.sentry.dev/mcp — no install. Datadog covers logs, metrics, and traces over time via a Claude Code plugin or an HTTP endpoint. Firecrawl’s own developer index answers whether someone else already hit the bug.
The connection commands are short:
claude mcp add --transport http sentry https://mcp.sentry.dev/mcpTwo details from the post are worth stealing. First, scope the Sentry connection to a single project rather than a whole account, so the agent cannot wander into unrelated data. Second, Datadog groups its tools by product area and lets you toggle them with /ddtoolsets; Firecrawl’s advice is to turn off everything you are not using, because an agent with full Datadog access will browse dashboards while you are chasing a null pointer.
The safety model is narrower than it sounds. Per Firecrawl, the Datadog server forwards the authenticated user’s own credentials, so the agent sees exactly what you see. Changing anything, like editing a monitor, needs a permission you grant explicitly. Firecrawl also notes rate limits of 50 requests per 10 seconds and 100,000 tool calls a month as of September 2026, and that it does not work on Datadog’s government cloud sites.
Split the question before you split the code
Firecrawl’s first worked scenario is a flood of 500s after a deploy, and the mistake it highlights is asking one big question. “Something is broken in production, go find it” hands the agent a wall of errors with no way to rank them.
Sentry’s cookbook recipe, as Firecrawl describes it, breaks this into three prompts: get the top five open issues from the last 24 hours with counts and affected users; go deep on the worst one with the latest stack trace, surrounding log lines, and a root cause pass; only then touch the code, and review the diff before anything changes. For a post-deploy spike, add two questions — when the error first appeared, and which release was live then. An error that started four minutes after a deploy is a different problem from one that has been firing for a month.
This is the same lesson that shows up in Cooley’s IPO agent work: the harness around the model, not the model, decides whether the output is usable.
Make the agent show its work
Firecrawl describes Devin’s testing loop in three steps: set up by reading the pull request and any project instructions, plan the single most important end-to-end flow and show that plan first, then run it with a screen recording and labeled screenshots. What comes back is evidence rather than a claim — a short report plus a full video with chapters and a pass/fail list.
The instruction wording decides whether any of it helps. Firecrawl quotes Devin’s docs contrasting a good prompt (“test checkout flow: add item to cart, proceed to checkout, fill form, verify order confirmation shows correct total”) with two bad ones (“test everything” and “make sure the app works”). The good version names a path and a correct result.
For a mobile layout regression, Firecrawl suggests screenshotting the affected page before and after at 1280px and 375px, then attaching all four images to the pull request. The reviewer compares pictures instead of reading a sentence that says “fixed.”
Firecrawl is explicit about the limit: this proves the page loaded and the flow ran. It does not prove the fix is well built or durable. Agent verification kills the “said it was fixed, never ran it” failure; it does not replace code review.
What to do with this
Start with the server-side source, because it tells you which bug you are actually chasing. Check the outside world second, since it can end the investigation before you edit anything. Open your own code last. And keep a human on the evidence — the loop is only closed when someone looks at the screenshots.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.