Code Context Engine

Name: Code Context Engine
Author: elara-labs

by elara-labsversion 0.4.13i★ –iRuns on your computer

Registry nameio.github.ai-elara/code-context-engine

Install

Overview

Description from the maker (original English)

Index your codebase. AI searches instead of re-reading files. 94% token savings.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi0 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 0 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "code-context-engine": {
      "command": "uvx",
      "args": [
        "code-context-engine"
      ]
    }
  }
}

Run this in your terminal

claude mcp add code-context-engine -- uvx code-context-engine

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "code-context-engine": {
      "command": "uvx",
      "args": [
        "code-context-engine"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "code-context-engine": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "code-context-engine"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

! Caution
Maker matches the repo
The publisher (ai-elara) is not the repo owner (elara-labs); someone else may have listed it
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 0% of the other 1385 servers in this category
Published to the registry more recently than 16% of the other 1385 servers in this category
Basic checks equal to or better than 23% of the other 1385 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Code Context Engine safe?

It passed 0 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.13) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Code Context Engine

Index your codebase. AI searches instead of re-reading files. 94% token savings, reproducibly benchmarked.

Website · Docs · Why CCE? · Benchmark · GitHub

Python 3.11+ · macOS · Linux · Windows

One command. Auto-detects your editor. Zero cloud, zero config.

Talk: We Cut 94% of Our AI Coding Tokens — AI Engineer World's Fair 2026

Use cases

	Use case	How CCE helps
💰	Reduce Claude Code costs	94% fewer input tokens per session
🔒	Keep code private	Everything local, no cloud indexing
🔄	Multi-editor teams	One index across Claude Code, Cursor, VS Code, Gemini CLI
🧠	Cross-session memory	Decisions and context survive restarts
⚡	Faster responses	Less context = faster Claude replies
📊	Track actual savings	Dollar amounts, not estimates

Quick start

One command. 30 seconds.

uvx --from "code-context-engine[local]" cce init    # install + index + configure, one shot

Or if you prefer a persistent install:

uv tool install "code-context-engine[local]"    # or: pipx install "code-context-engine[local]"

cd /path/to/your/project cce init

Restart your editor. Done. Every question now hits the index instead of re-reading files.

Agent Plugin support: Run cce init --plugin to generate a portable

Agent Plugin directory that works with

VS Code, Cursor, Copilot, Codex, ChatGPT, and Kiro. The plugin uses

uvx to launch CCE on demand, so users don't need to pre-install the

Python package. See Agent Plugin below.

Already have Ollama? Skip [local] and use uv tool install code-context-engine instead. CCE auto-detects Ollama at localhost:11434 and uses nomic-embed-text.

System requirements

Python 3.11+ and a C compiler (for tree-sitter grammars).

Platform	Setup
macOS	`xcode-select --install`
Ubuntu/Debian	`sudo apt install build-essential cmake`
Fedora/RHEL	`sudo dnf install gcc gcc-c++ cmake`
Windows	Visual Studio Build Tools (C++ workload) + CMake

Tested on macOS, Linux, Windows with Python 3.11/3.12/3.13.

cce init auto-detects your editor and writes the right config. To target a specific agent, use --agent claude, --agent codex, --agent copilot, --agent pi, or --agent all.

Editor	Config written	Instructions
Claude Code	`.mcp.json`	`CLAUDE.md`
VS Code / Copilot	`.vscode/mcp.json`	`.github/copilot-instructions.md`
Cursor	`.cursor/mcp.json`	`.cursorrules`
Gemini CLI	`.gemini/settings.json`	`GEMINI.md`
OpenAI Codex	`~/.codex/config.toml` (user-global, per-project section)	`AGENTS.md`
OpenCode	`opencode.json`
Tabnine	`.tabnine/agent/settings.json`	`TABNINE.md`
Pi	`.mcp.json`	`AGENTS.md`

Multiple editors in the same project? All get configured in one command.

Codex note: Codex CLI reads MCP servers from ~/.codex/config.toml only — it has no per-project config. cce init adds one [mcp_servers.cce- - ] section per project so multiple projects coexist; cce uninstall removes only the section for the current project.

Pi note: Pi does not support MCP natively. To use CCE with Pi, you need a pi MCP adapter extension (e.g. pi-mcp-adapter) that consumes the .mcp.json config and exposes CCE's tools to the Pi agent. cce init sets up both .mcp.json and AGENTS.md (Pi loads the latter automatically for startup instructions).

  my-project · 38 queries · last query 5m ago

⛁ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ 88% tokens saved

Input savings 1.9M tokens $27.78 Output savings 4.8k tokens $0.36 ────────────────────────────────────────── Total saved 1.9M tokens $28.15

Breakdown: retrieval 84% ▰▰▰▰▰▰▰▰▰▰ 1.8M $26.76 · 12 calls chunk compression 3% ▰▱▱▱▱▱▱▱▱▱ 68.5k $1.03 · 12 calls output compression* <1% ▰▱▱▱▱▱▱▱▱▱ 4.8k $0.36 · 12 calls

Cost estimate based on Opus pricing (input $15.0/1M, output $75.0/1M)

Supports Anthropic, OpenAI, and Google model pricing. Configure via pricing.model in ~/.cce/config.yaml.

Why this matters

Input tokens are 85-95% of your Claude Code bill. CCE cuts them by 94% (benchmarked on FastAPI).

Without CCE:    Claude reads payments.py + shipping.py   = 45,000 tokens

With CCE: context_search "payment flow" = 800 tokens

	Without CCE	With CCE
Session startup	Re-reads files every time	Queries the index
Finding a function	Read entire 800-line file	Get the 40-line function
Cross-session memory	None	Decisions + code areas persisted
Token cost (Sonnet, medium project)	~$0.14/session	~$0.04/session

Benchmark: FastAPI (reproducible)

We benchmarked CCE against FastAPI (53 source files, 180K tokens) with 20 real coding questions. No cherry-picking, no synthetic queries.

Methodology: For each query, "without CCE" means reading the full content of every file the query touches. "With CCE" means the relevant chunks after compression.

Important baseline note: The 94% number is measured against full-file reads, not against what Claude Code actually does. In practice, Claude Code already uses grep, partial file reads, and targeted tools, so the real-world savings compared to normal Claude Code behavior will be lower than 94%. We use full-file as the baseline because it's reproducible and deterministic (no agent behavior variability). The benchmark measures CCE's retrieval efficiency, not a head-to-head comparison with Claude Code's built-in exploration.

Metric	Result
Retrieval savings	94% (83,681 → 4,927 tokens/query)
Compression (additional, on retrieved chunks)	89% (4,927 → 523 tokens/query)
Recall@10 (found the right files)	0.90
Latency p50	0.4ms
Queries tested	20

Per-Layer Savings (each measured independently)

Layer	What it does	Savings	Method
Retrieval	Full files → relevant code chunks	94%	measured
Chunk Compression	Raw chunks → signatures + docstrings	89%	measured
Grammar	Drops articles/fillers from memory text	13%	measured

Output compression (reducing Claude's reply length) provides additional savings (~65% estimated) but is not included in the headline number above.

Multi-language benchmarks

Repo	Language	Files	Retrieval savings	Recall@10
FastAPI	Python	53	94%	0.90
Django	Python (large)	2,347	93%	0.95
Express	JavaScript	6	94%	1.00
chi	Go	94	76%	0.67
fiber	Go (monorepo)	396	93%	0.07

Django (2,347 files, 5.4M tokens) shows CCE scales to large codebases with 0.95 recall. Go's shorter files reduce the retrieval headroom (smaller baseline). Monorepos dilute recall at top-10 (fiber). Middleware queries with one-feature-per-file hit R=1.00 consistently.

Reproduce it yourself:

pip install code-context-engine

python benchmarks/run_benchmark.py --repo https://github.com/fastapi/fastapi.git --source-dir fastapi python benchmarks/run_benchmark.py --repo https://github.com/go-chi/chi.git --source-dir .

Full results in benchmarks/results/. Queries and methodology in benchmarks/.

What you get

11 MCP tools that Claude uses automatically:

Tool	What it does
`context_search`	Hybrid vector + BM25 search with graph expansion

Only the beginning is shown; read the rest in the repo

Code Context Engine

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

Use cases

Quick start

Why this matters

Benchmark: FastAPI (reproducible)

Per-Layer Savings (each measured independently)

Multi-language benchmarks

What you get