What they have in common
Both Augment Code and Context Engine solve the same underlying problem: coding agents cannot read your whole repository, and brute-force search wastes tokens and context. Both build a semantic index of your codebase and expose it to agents over the Model Context Protocol (MCP), so the agent can ask for context instead of grepping.
If you have used Augment’s Context Engine MCP and liked the idea, Context Engine will feel familiar — the difference is in the details below. That shared foundation, a semantic index exposed over MCP, is also why evaluating one against the other takes minutes rather than a rewrite of how your agent works.
Where Context Engine is different
Context Engine is built around a token budget you control on every call, and it returns file-level spans rather than whole files — so you can cap what each retrieval costs. It exposes a single MCP tool, codebase_context, to any MCP-compatible client: Claude Code, Cursor, Codex, Windsurf, and others. Sign-in is GitHub, and there is a free quota so you can evaluate it on your own repo before committing. There is no separate desktop app or IDE fork to install — the surface area is one tool call, which keeps the thing you have to trust and debug small.
- A hard token budget per retrieval, returned as file-level spans
- Works with any MCP client, not tied to one IDE or agent
- One tool to reason about — codebase_context — nothing else to wire up
- GitHub sign-in with a free quota on signup
- Incremental sync and a feedback loop that tunes retrieval quality
When Augment Code may be the better fit
Augment Code is a broader, enterprise-oriented platform with its own agent and IDE integrations, and it is a strong choice if you want an all-in-one coding assistant with vendor support at that scale. Context Engine is deliberately narrower: it does one thing — token-budgeted codebase context over MCP — and lets you keep the agent you already use. Pick the tool that matches how you want to work; you can even run Context Engine alongside another assistant to compare them directly on your own tasks before standardizing on one.
Why we built a narrower tool instead of a platform
Augment Code’s pitch is broad: an agent, an IDE plugin, and a context engine bundled together. That is a reasonable choice if you want one vendor to own the whole workflow. Context Engine started from a narrower complaint — that the context layer, the part that decides what code an agent gets to see, was worth building on its own and exposing to whatever agent you already use, rather than bundling it with a new one.
That choice shows up in what you configure and what you do not. There is no separate IDE to install, no agent to switch to, and no workflow to relearn — just one MCP tool that returns a token-budgeted context pack to whatever client calls it. If you are already happy with Claude Code, Cursor, or another agent, this is the piece you add rather than the platform you migrate to.
Trying it is an evaluation, not a migration
Trying Context Engine next to Augment Code, or next to anything else, does not require ripping anything out. Add the MCP config block, point it at the same repo, and run a few of the tasks you would normally run — a trace-the-call-site question, a refactor, a bug hunt. Because the token budget and the spans returned are visible per call, you can compare cost and answer quality side by side instead of taking either tool's word for it.
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