# Give an AI agent context of a large repo without a local indexer

Canonical source: https://maguyva.ai/blog/large-repo-context-without-a-local-indexer

License: text under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); code samples under [MIT-0](https://opensource.org/license/mit-0); the Maguyva name, logos, brand assets and other code are not covered. Details: [maguyva.ai/license](https://maguyva.ai/license).

Date: 2026-08-14 · Author: Maguyva Team

> How to give a coding agent a queryable map of a large repository from the cloud, without running a local indexer on your laptop.

A large repository doesn't fit in a context window, and if you've ever pointed a laptop indexer at a real monorepo you know it doesn't fit comfortably there either — the RAM spikes, the first index takes an hour, and your agent still starts the next session guessing which files matter.

The usual answer is to run an indexer next to the editor, and for a repo that fits comfortably on one machine, it's a good one. If your monorepo already indexes fast in Cursor and that's the only place you need the map, this post has nothing to add — skip it.

This post is about the other case — when you need an agent to have a queryable map of a large repo, and you don't want to run a local indexer to get it.

## When a local indexer is the wrong tool

A local index assumes the code lives on the machine that will query it, and at real monorepo scale that assumption strains even inside a single editor. The repo is simply bigger than you want to keep warm on a laptop: you work in one package while the blast radius lives three directories over, and a cold first index of a two-million-line tree is not something you want to sit through twice — on your machine, or on a CI runner that never cloned the tree before. Or a teammate should be querying the same map you are, without waiting for their own machine to grind through the same index first.

None of that is a reason to throw away a good editor indexer. It's a reason to also have a hosted map that any of your agents can query over MCP.

And the scale problem runs deeper than "too many files to paste." On a large tree, the interesting question is almost never "show me this file" — it's which file, which symbol, which callers, which tests. An agent that starts by listing `src/` and reading the first three hits will happily miss the handler in `apps/api` and the generated client in `packages/sdk`. What you need is retrieval that was built from the whole tree, then ranked, then cited.

## What "context of a large repo" actually means

Dumping files into the prompt isn't context — it's a smaller, worse copy of the tree. What your agent actually needs is to ask questions and get cited answers back: where is this symbol defined and who calls it, what depends on the module I'm about to change, which test file covers this behavior, and what does the tree look like *now*, not last quarter.

Those answers have to come from an index built from your repo rather than from the model's memory of other people's repos, and the index has to live somewhere other than the agent's context window. For this problem, it should also live somewhere other than your laptop.

One more thing instruction files can't do for you: `CLAUDE.md` and `AGENTS.md` are the right place for conventions and behavior, but they are not a live, ranked, queryable map of a two-million-line tree. Use rules for behavior; use repo intelligence for facts.

## Build the map in the cloud, query it from any client

Maguyva is a remote MCP codebase context layer. You connect GitHub, pick your repositories, and walk away while cloud pipelines parse the tree, build the symbols and graph edges, and keep a ranked map — no local indexer, ever.

When you push code, changed files are parsed incrementally, and the graph and ranking layers rebuild on a schedule: daily on the free plan, hourly on paid ones. Every snapshot is timestamped, so your agent can tell which version of the tree it's reading. The honest limit here is un-synced local work — an edit that exists only in your working tree, never pushed, isn't in the hosted map yet.

Then you wire up whichever client you already use by adding an API key to its MCP config. There are setup guides for 24 documented MCP clients, and any other MCP-compatible client that supports standard configuration can connect too. If Cursor is where you live, the [Cursor page](/for-cursor) walks that path; the same server answers Claude Code, VS Code, Windsurf, Codex CLI, and the rest.

You don't switch editors. You give your agent a tool surface — 11 MCP tools across search, symbols, dependency analysis, file retrieval, and repository context — with coverage across 279+ languages and text-based technologies, which matters more than you'd think on a monorepo that stopped being one language years ago.

Answers come back grounded, as file paths with line numbers. Semantic, structural, graph, and text retrieval are fused into ranked results, so a question like "what depends on parseConfig" gets you neither a grep dump nor a single semantic chunk, but the handful of locations that actually matter.

## A concrete how-to

Don't start by indexing the whole company. Here's the path that works:

1. Pick one repository you already know well — a package you can grade by hand beats the largest tree you have.
2. Start free: 3 repos, 50k indexed lines, all 11 tools, no monthly query quota. If your representative repo is bigger than that, the [pricing page](/pricing) lists the workspace tiers ($0, $19, $49, $149) by indexed footprint — not by seat count.
3. Connect GitHub and wait for the first index to finish. You're not compiling anything locally.
4. Wire the MCP client — a few lines of config. Maguyva is a remote MCP server, so there are no plugins, no extensions, and no second editor to adopt.
5. Ask one question you can verify. "What depends on parseConfig?" or "Where is login session expiry handled?" Then open the cited `file.ext:line` results and check them against the tree in front of you.
6. Only after the citations survive that contact do you let the agent edit.

If the first repo is a good fit and your tree is large, raise the workspace to match your indexed lines and rebuild cadence. Humans and agents share the same user count — Maguyva bills per workspace, not per seat — so adding a third agent to the loop costs you nothing.

The activation test is the same one the [docs](/docs) recommend for any first run: index one repo you already know, ask one question you can verify. A large repo doesn't change the test; it only makes a wrong first file more expensive.

## What to watch after it's wired

Keep an eye on three things. Freshness: paid plans rebuild hourly, free rebuilds daily, and un-pushed local work will not be in the map — that's the trade you're making. Scope: index a representative set of repos, not every archived fork; the free tier is deliberately sized for finding out whether the answers are worth keeping. And verification: a large-repo map that can't cite paths and line numbers is just a slower guessing engine, so make your agent show you the location before it ships the patch.

Then run the same test you'd run on a small repo. Index one repo you already know. Ask one question you can verify. If the map is real, your agent stops starting from a cold tree.

`// no laptop got hot building this map.`