Context for Cursor: Beyond the Open Files

Context for Cursor goes beyond the open files. Learn how to feed the editor your company's knowledge via MCP so it codes like a teammate.

By Founder of CtxFlow

Context for Cursor: Beyond the Open Files

Context for Cursor is everything the editor’s AI reasons over — and by default that is mostly your open files, the workspace it has indexed, and what you type. That is enough for autocomplete and local edits. It is not enough for the editor to understand why your codebase is shaped the way it is: the architecture decisions, conventions, and internal docs that live outside the repo. To make Cursor code like a teammate who already read those, you connect your company’s knowledge through the Model Context Protocol (MCP), so the editor can pull the right slice on demand.

This guide explains what Cursor actually sees, why the open files leave a gap, how Cursor’s own rules files help and where they stop, and how an MCP-native context layer feeds it your company’s knowledge without copy-paste.

Key takeaways

In this guide

What context does Cursor see by default?

By default, Cursor reasons over your open files, the parts of your workspace it has indexed, recent edits, and the instructions you give it. That local view powers fast, in-editor suggestions.

What it does not include is the knowledge that explains the code. Cursor does not automatically know your team’s testing conventions, the decision behind a database choice, or the contract your internal API is supposed to honor. Those facts are not in the files it indexes — they live in docs, wikis, decision records, and tickets. The same limitation applies across coding surfaces, which is why feeding context to Claude Code follows the same pattern.

It’s worth being precise about what “indexed” buys you. Cursor’s workspace index makes your code searchable to the model — it can find the function you mentioned without you opening the file. That’s genuinely useful, and it covers the “what.” But indexing the repo does nothing for knowledge that was never in the repo. The index is a faster path to files you already have; it is not a path to the decision log, the runbook, or the design doc. Those need a different mechanism entirely.

Why do the open files leave a context gap?

The open files describe what the code is. They rarely capture why it is that way.

A human engineer joining your team reads the architecture doc, skims past decisions, and learns the conventions before writing a line. Cursor, working only from indexed files, skips that step. So it can suggest code that compiles but breaks an unwritten rule — an invented config key, a deprecated pattern, a call that ignores your internal contract. The problem is not the model’s ability. It is missing context.

The failure is sharper in an editor than in a chat, because Cursor produces code inline, fast, often a tab-completion away from being committed. There’s no natural pause where you’d notice “the model didn’t know about our error-handling rule.” It just writes the plausible-looking version, you accept it, and the unwritten rule is now violated in your codebase. Speed is the feature and the risk: the faster the editor writes, the more it matters that what it writes is grounded in your conventions, not the average of its training data.

What about .cursor/rules — do they fill the gap?

Partly — and it’s worth knowing exactly how far they go. Cursor lets you define project rules as .mdc files under .cursor/rules/ (the modern replacement for the legacy single .cursorrules file). Rules are standing instructions: coding standards, framework conventions, patterns to prefer or avoid. Cursor supports several activation modes — always-apply, auto-attached by file glob, agent-requested by description, or manual — so a rule can fire only when the agent touches matching files (Cursor’s rules documentation covers the formats and modes).

Rules are excellent for instructions and weak for knowledge. “Use our error-handling wrapper on every API call” is a rule — short, stable, applies broadly. “Here is the 4,000-word decision record explaining why we chose this database and the twelve constraints that follow from it” is not a rule; it’s a document, and pasting it into a .mdc file makes that file part of the prompt on every matching edit, bloating the window with text most tasks don’t need. The same lesson that applies to Claude Code’s CLAUDE.md applies here: keep rules lean and standing; put the knowledge base behind retrieval the editor pulls on demand. Rules tell Cursor how to behave; a context layer tells it what’s true about your system right now.

What company knowledge should Cursor have access to?

The relevant slice, not everything. The high-value categories for a coding editor:

Choosing which of these to surface for a given task is context engineering — the discipline of deciding what enters the window and what stays out. The aim is grounding, not volume. A simple test for any candidate document: would you ask a new hire to read it before their first PR? If yes, it’s the kind of knowledge Cursor benefits from reaching. If it’s unrelated to how the code works, it’s noise that competes with the signal.

How do you give Cursor your company’s context?

You connect it through an MCP server — a server that exposes your knowledge as tools the editor can call. Cursor has built-in MCP support, configured through its settings or a project mcp.json file, and the same server packages work across MCP-speaking tools.

At a high level:

  1. Stand up an MCP server that exposes your company knowledge.
  2. Register it in Cursor so the editor sees its tools.
  3. Let Cursor query on demand — it fetches the relevant doc or decision when a task needs it.

Notice how this composes with project rules rather than competing with them. Rules handle the always-on “how to behave”; the MCP server handles the on-demand “what’s true.” A well-set-up team uses both: a lean set of .cursor/rules for standing conventions, and a connected knowledge layer for the long tail of decisions and docs. We walk through the setup and patterns in detail in MCP context in Cursor, and the broader how-to in giving Cursor your company’s codebase context.

A worked example: the convention Cursor couldn’t see

Say you ask Cursor to add a new API route. It looks at your existing routes, mirrors their shape, and writes a handler that throws a raw error on bad input. Reasonable — except your team decided months ago that every route wraps failures in a typed ApiError so the client gets a consistent shape, and that decision lives in a design doc, not in any single route file the editor indexed. Cursor produced code that matches the files and breaks the convention. Inline, fast, accepted before you noticed.

Replay it with the decision reachable through an MCP server. As Cursor drafts the handler, the agent retrieves the “API error handling” decision and writes the route with the ApiError wrapper, the way your team does it. The model didn’t get smarter — it got the missing fact. The lesson generalizes: most of Cursor’s “wrong but plausible” output isn’t a reasoning failure, it’s a supply failure, and the fix is to make the relevant knowledge reachable rather than hope it’s in the indexed files.

Why not just paste the docs into Cursor’s chat?

Pasting works for one question and breaks at scale. Two problems surface quickly.

First, you are the bottleneck — every answer is only as good as the snippet you remembered to attach. Second, more context is not better. Long prompts have a known weak spot: the material in the middle gets used far less reliably than what sits at either end, which is what the Stanford Lost in the Middle study (Liu et al., 2023) measured. Pour your whole wiki into the window and you mostly raise the cost while hiding the part that mattered.

There’s a third problem specific to an editor you use all day: pasting doesn’t scale across sessions. You’d have to re-paste the same conventions every time you open a new chat or switch files. A connected layer is paste-once-and-forget — the knowledge is reachable for every task without you reconstructing it. The goal is the right context, freshly scoped — a trade-off we cover in how much context an AI agent actually needs. An MCP-backed layer lets Cursor pull exactly what a task needs instead of you front-loading a wall of text.

How do you keep Cursor’s context scoped and current?

Scope it at the source and let the editor retrieve rather than receive.

A good context layer returns the relevant slice of a large knowledge base, respects who can see what, and reads from where knowledge actually lives so it never goes stale. Those are also the qualities to weigh when you choose an MCP server for coding. Done well, Cursor reaches a large body of company knowledge while its working window stays lean.

Currency matters more than it looks in a coding tool. A convention that changed last week, served from a stale snapshot, will have Cursor confidently writing the old pattern — and because the editor writes inline and fast, that outdated pattern propagates before anyone catches it. Reading from the live source means the editor always reflects the latest decision, not a frozen copy. Permission awareness is the other non-negotiable: the editor should reach only what the requesting user already can, so connecting knowledge changes how you reach it, never what you’re allowed to see.

Common mistakes giving Cursor context

Where a context layer comes in

Everything above points to the same fix: stop hand-feeding Cursor and give it a source it can reach itself. A context layer does exactly that — it connects your decisions, conventions, and docs once and lets the editor pull the relevant slice on demand, scoped and current, across whatever other AI tools your team runs. Connect once instead of pasting forever, and Cursor codes to your standards instead of to its training data. That shared layer is what we’re building at CtxFlow, if you want to see the approach in practice.

FAQ

What context does Cursor use by default? Cursor reasons over your open files, the workspace it has indexed, recent edits, and your prompts. That covers local edits and autocomplete but excludes company knowledge — decisions, conventions, and docs — that lives outside the repo.

Do .cursor/rules give Cursor my company context? Partly. Rules carry standing instructions — conventions, patterns, standards — and fire by glob or description. But they’re for short instructions, not your knowledge base. Pasting long docs into them bloats the prompt; connect that knowledge through an MCP server instead.

Why does Cursor suggest code that breaks our conventions? Because those conventions usually aren’t in the files Cursor indexes — they live in docs and decision records it can’t see by default. Connecting that knowledge through MCP lets the editor honor the rules instead of guessing.

How do I give Cursor access to docs outside my repo? Connect them through an MCP server. Cursor has built-in MCP support, configured via settings or a project mcp.json, so the editor can fetch the relevant doc or decision on demand rather than relying on what you pasted.

Is it better to attach more files to Cursor? No. Beyond what the task needs, more context degrades accuracy through lost-in-the-middle effects and raises cost. Attach what the task touches and connect the rest through a server the editor can query.

Can one context layer serve both Cursor and Claude Code? Yes. Both speak MCP and share the same server format, so a single well-built context layer can feed multiple coding tools — and other AI surfaces — from one source of truth.

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