AI IDE Context Problems and Fixes

When a run goes wrong, resist the urge to re-prompt and hope. Narrow it to one symptom first, then make the smallest change that tells you whether you guessed right.

1. 1Stop the run and save the current diff.
2. 2Check whether the task was too broad or missing files.
3. 3Read the failing command, log or review comment out loud.
4. 4Start a smaller run with one clear done state.
5. 5Run the narrowest check before asking for more changes.

Save the diff before you do anything else. Even a run that went badly usually has an edit or two in it that were right, and if you throw the whole thing away you'll just pay for that work again. It's also far easier to read than the transcript when you're trying to work out where things went sideways.

The instinct is to re-prompt, harder. Look, it almost never works. If the agent has already misread the task, adding "please be careful" to the same prompt gives it precisely nothing it didn't have the first time. What actually changes the outcome is new information: the file it never opened, the error text you paraphrased instead of pasting, a boundary tight enough that the ambiguity disappears. When the second attempt fails the same way as the first, that's the prompt telling you something, not the model.

Most of these failures trace back to a gap the run never closed. Usually the agent missed the context that mattered, the prompt never set a boundary, or nothing checked the result before it landed.

Add the exact thing that broke to the chat. Copy the failing cell, the error output, or the terminal error and use add to chat, so the agent gets precisely what you ran instead of a paraphrase. This is the single most effective habit for pinpointing where an issue happens.

Check the index before you touch the prompt. Cursor starts indexing when you open a workspace, and semantic search only becomes available at 80% completion, with a progress indicator in the status bar. A weak answer in the first minute after a fresh clone may just be a half-built index, so ask again before concluding anything about the model.

Two other bits of index state produce the same symptom. The index syncs automatically every 5 minutes and processes only changed files, so a file you created moments ago may not be retrievable yet.

And if you haven't opened the project in 6 weeks, the hosted index is deleted; reopening triggers a fresh one.

All three are dull to check and that is the argument for checking them. I'd guess most "the agent ignored my file" reports are one of those states rather than a prompting problem, and none of them takes more than a few seconds to rule out.

Usually because something excluded it. Cursor keeps anything matched by .gitignore or .cursorignore out of the index, along with binary files and files above its size limit, so a generated schema or an env-shaped config can be invisible for entirely mundane reasons. Check the ignore files first, because .cursorignore blocks @-mentions too, so unignoring the path is the only fix there. An @ mention does work on a file that is merely too new or too large to have been indexed.

It is tempting to then treat .cursorignore as the access control, and Cursor's own description invites that: it excludes files from semantic search, agent file reading and context selection. Cursor also says plainly that it is a convenience feature and not a security boundary. Users can still open ignored files by hand, and terminal and MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. tools can't honor it.

So it's the right tool for keeping a noisy build directory out of retrieval, and the wrong one for keeping a secret away from an agent. For that you want file-system permissions, or the data encrypted.