Boosting Claude Faster Clearer Code Analysis With Mgrep
Boosting Claude: Faster, Clearer Code Analysis with MGrep
I tested whether a stronger search tool could improve an LLM's ability to understand a codebase. With one instruction to use mgrep, a semantic search tool by mixedbread, Claude got faster, more efficient, and more accurate.
Note: Mixedbread goes beyond traditional single-vector hybrid search + reranker; it's multi-vector, multi-modal search. This isn't the same semantic search that fell out of favor for agents.
I asked Claude to explain a complicated feature I'm building, using the
same prompt twice. The first run was standard; the second added one
instruction about mgrep. Here's a side-by-side video of one run. In
this post I'll break down mgrep and how it changes Claude's
performance.
What does Mgrep do?
mgrep is a grep-like tool that uses semantic search. Basic usage looks like this:
The A/B Test Setup
I wanted the AI to explain image handling, UX, and editor architecture in my application.
Prompt A (Standard Claude):
Can you explain the image, UX, and the editor, and all the things that are done to support that?
Prompt B (Claude + mgrep):
Same prompt, with one addition:
Use mgrep extensively for search. It's the most powerful semantic search tool. Use it like
mgrep "whatever you want to search for". Start with that before diving deeper.
The Numbers: Faster and More Efficient
The results were clear: better search made the process faster and lighter. Since it's not fully deterministic, I ran three trials.
Speed:
-
Standard Claude runs:
- 1 minute, 58 seconds
- 2 minutes, 28 seconds
- 4 minutes, 7 seconds
-
Claude +
mgrepruns:- 1 minute, 6 seconds (56% of standard time)
- 1 minute, 48 seconds (73% of standard time)
- 1 minute, 48 seconds (44% of standard time)
The mgrep version was nearly twice as fast.
Efficiency (Agent History File):
-
Standard Claude:
- 4,984 lines
- 4,868 lines
- 5,626 lines
-
Claude +
mgrep:- 2,061 lines (41% of standard lines)
- 1,948 lines (40% of standard lines)
- 2,549 lines (45% of standard lines)
The mgrep-assisted run used less than half the context. That means fewer tokens, less processing, and a more focused analysis.
The Analysis: Better Insight, Accuracy, and Structure
The mgrep response was faster and more insightful, accurate, and
structured. I compared the first versions, since that's what I'd use in
practice.
High-Level Insight from the Start
The mgrep response showed a better understanding of the feature.
-
Standard Claude started with a generic description: "the editor supports images through multi-layered architecture."
-
Claude + mgrep was specific and immediately useful: it identified the TipTap React editor and the gallery's two core modes, "selection" and "gallery change."
That description is more valuable to me. It gets straight to how the feature works.
Improved Technical Accuracy
Deeper in the analysis, standard Claude made a subtle but significant error: it described two ways to enter the full-screen gallery as separate features. The mgrep version correctly saw two triggers for the same action.
A More Logical Flow
The report structure also revealed a major difference.
The mgrep response was logical: it started with front-end UX, then moved through back-end routes, the storage layer, and markdown handling.
The standard Claude response was more scattered, jumping between front-end UX, back-end details, and another front-end component.
For example, my app uses a two-tier image URL strategy. The mgrep response explained the intent: fast, pre-signed URLs for thumbnails and a stable proxy for permanent images. Standard Claude presented the raw JSON, which was less helpful for what I asked.
The tools we give our AI assistants matter.
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Better Tools, Better Results: A semantic search tool like mgrep yields faster, more efficient, higher-quality analysis.
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Efficiency is a Quality Signal: The mgrep version used less than half the context. This wasn't a shortcut; it was a more direct path to the answer.