Nexus-MCP

Name: Nexus-MCP
Author: jaggernaut007

by jaggernaut007version 2.3.0i★ –iRuns on your computer

Registry nameio.github.jaggernaut007/nexus-mcp

Install

Overview

Description from the maker (original English)

Local code-intelligence server: hybrid code search, call graph and project memory.

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 checksi1 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: Search & AI memory

Install

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

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: NEXUS_STORAGE_DIR, NEXUS_OFFLINE

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": {
    "nexus": {
      "command": "uvx",
      "args": [
        "nexus-mcp-ci"
      ],
      "env": {
        "NEXUS_STORAGE_DIR": "your-value",
        "NEXUS_OFFLINE": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add nexus --env NEXUS_STORAGE_DIR=your-value --env NEXUS_OFFLINE=your-value -- uvx nexus-mcp-ci

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "nexus": {
      "command": "uvx",
      "args": [
        "nexus-mcp-ci"
      ],
      "env": {
        "NEXUS_STORAGE_DIR": "your-value",
        "NEXUS_OFFLINE": "your-value"
      }
    }
  }
}

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": {
    "nexus": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "nexus-mcp-ci"
      ],
      "env": {
        "NEXUS_STORAGE_DIR": "your-value",
        "NEXUS_OFFLINE": "your-value"
      }
    }
  }
}

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.

Values you fill in

NEXUS_STORAGE_DIR env
Folder for the index, memories and graph (default: .nexus in the project)
NEXUS_OFFLINE env
Set to 1 to block the embedding model download after the first run

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account jaggernaut007, which owns the repo
… 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 Search & AI memoryi

More stars than 0% of the other 5650 servers in this category
Published to the registry more recently than 98% of the other 5650 servers in this category
Basic checks equal to or better than 100% of the other 5650 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 Nexus-MCP safe?

It passed 1 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 (2.3.0) reached the registry yesterday.

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: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

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.

Nexus-MCP

Hybrid search + code graph + semantic memory in a single local MCP server. About 90 MB idle and 460 MB with the embedding model loaded ([measurements](https://github.com/jaggernaut007/Nexus-MCP/blob/main/docs/MEMORY.md)).

Nexus-MCP is a local code-intelligence MCP server for Claude Code, Codex, Cursor and any Model Context Protocol client. It gives AI agents precise, token-efficient answers about your codebase without cloud dependencies: no API keys, no data egress, no subscriptions.

Not sure yet? Measure it on your own code first. It is free, runs offline, calls no LLM and takes a few minutes:

uvx nexus-mcp-ci eval            # the current folder

uvx nexus-mcp-ci eval --example # or the Nexus-MCP source, if you have no project at hand

You get a verdict ("Strong fit", "Likely fit" or "Weak fit") and the tokens an agent needs to reach the right file, with and without Nexus-MCP. Nothing leaves your machine. See Evaluate on your project.

Ready?

pip install nexus-mcp-ci

claude mcp add nexus-mcp -- nexus-mcp-ci

Results at a glance

Project	Cost: Grep and Read → Nexus-MCP	Turns	Correct answers
Private Python project (460 files)	$0.571 → $0.446 (-22%)	55 → 35	36 → 35 of 36
Django (3,000 files)	$0.894 → $0.863 (-3%)	93 → 71	34 → 36 of 36

Live runs: Claude Code, Claude Sonnet, headless (claude -p), 12 questions per project, 3 runs each, version 2.1.0 (2026-10-28). The same code can vary by about 20% in cost with 3 runs per question (measured 2026-10-28). Search hit@1 on the four retrieval suites: jobscout 0.81, nexus_mcp 0.80, shop_repo 0.50, flask 0.64.

nexus-mcp-ci eval (the free offline check, no LLM) on 3 projects:

Project	Verdict	Median tokens to the right file	Right file first
Chromium base/ (3,638 files)	Strong fit	17,998 → 950	40% → 70%
pallets/click (91 files)	Strong fit	24,774 → 1,301	27% → 97%
Nexus-MCP source (59 files)	Likely fit	5,385 → 1,234	70% → 93%

Nexus-MCP and GitNexus on Chromium base/ (C++, 4,243 files): 8 tasks, 3 runs each, 2026-11-04.

Condition	Version	Tokens (median)	Cost per task (median)	Correct
Grep and Read	built-in tools	62,450	$0.057	24 of 24
Nexus-MCP	2.2.0 with the C++ parser fixes of 2.2.1	82,900	$0.062	24 of 24
GitNexus	1.6.12	164,192	$0.085	24 of 24

Nexus-MCP used fewer tokens than GitNexus on 8 of 8 tasks and cost less on 6; GitNexus cost $0.002 less on one task, and one task was a tie. Grep and Read cost less than both on this C++ code. Nexus-MCP cost less than Grep and Read on 3 of 8 tasks (the two needle tasks and WeakPtr). Each condition had the same agent, model, prompt and built-in tools, and each tool had its MCP server and its always-loaded instructions (method).

All numbers: benchmarks page · JSON · CSV.

The Problem It Solves

An agent that explores a codebase with Grep and Read pays for every call: each call is one more turn, and each turn reads the whole conversation again. Nexus-MCP answers a question about code in fewer calls. search returns the whole code of its top results, find_symbol returns the code of several names in one call, and graph returns every text reference beside the call edges.

The north star of the project is lower token use with the same accuracy. It is measured, not estimated: see Token efficiency and benchmarks/.

Evaluate on your project

Decide with your own numbers. The quick check is free, offline and calls no LLM:

uvx nexus-mcp-ci eval                                   # the current folder

uvx nexus-mcp-ci eval path/to/project # another folder uvx nexus-mcp-ci eval https://github.com/pallets/flask # a public repo (shallow clone) uvx nexus-mcp-ci eval --example # the Nexus-MCP source, offline

It asks about 30 questions made from your own code and compares Grep and Read with one Nexus-MCP search. The right answer is the file that defines the symbol, from the parser, never from the Nexus-MCP ranking. On this repo's source (59 files) it gave "Likely fit": 1,234 median tokens to reach the right file against 5,385, and the right file first in 93% of questions against 70%. On Chromium's base/ (3,638 files of C++, Java and Python) it gave "Strong fit": 950 median tokens against 17,998 (95% fewer), and the right file first in 70% of questions against 40%. On pallets/click (91 files, from its URL) it gave "Strong fit": 1,301 median tokens against 24,774 (95% fewer), and the right file first in 97% of questions against 27%. On a tiny project Grep and Read win, and the report says so.

Want the real agent cost? The agent tier runs your own Claude Code or Codex login on tasks from your code, with and without Nexus-MCP. It shows the cost first and asks:

nexus-mcp-ci eval --agent claude                       # Claude Sonnet

nexus-mcp-ci eval --preset efficient --agent claude:haiku,claude:sonnet

Results are saved in ~/.nexus-mcp/evals/ (eval list, eval show ). eval share posts the numbers (no code, no paths) to the community table, only after you say yes. Full guide: docs/EVALUATE.md. In Claude Code, the plugin adds /nexus-mcp:evaluate.

Privacy and Data

Your code stays on your machine. The server makes no network calls and has no telemetry.

In: the package from PyPI, and the embedding model from Hugging Face once (set NEXUS_OFFLINE=1 after that). eval also runs a git clone.
Out: nothing, unless you run eval --agent (prompts and code go to Anthropic or OpenAI through your own CLI) or eval share (numbers only). Both ask first.
Stored: the index, memories and graph in /.nexus/ (it holds code: add it to .gitignore), the model in ~/.cache/huggingface/, eval results in ~/.nexus-mcp/evals/.

nexus-mcp-ci data where              # every location and its size

nexus-mcp-ci data clear --all # dry run; add --yes to delete

Every location, the shell commands that do the same, and the uninstall steps: docs/PRIVACY.md.

Quickstart (60 seconds)

You need uv. Nothing else.

Ask your agent. Open your agent (Claude Code, Codex, Cursor, Gemini CLI) in your project and say:

Install Nexus-MCP from https://github.com/jaggernaut007/Nexus-MCP

The agent reads llms-install.md and runs one command. You can run the same command yourself:

uvx nexus-mcp-ci setup

setup finds the MCP clients on your machine, shows each change, and asks before it writes anything. It adds the server and a short skill that tells the agent when to use each tool. Add --dry-run to see the changes only, --client codex to pick one client, and --uninstall to remove what it wrote. Then reload your client (or start a new session) and ask something like "where do we retry failed payments?". The agent calls search, and index once, the first time in a project.

The server sends its own usage instructions when it connects, so the agent knows which tool answers which question even without the skill.

Other ways in, each with the same server:

VS Code: Install in VS Code
Cursor: the "Add to Cursor" button on the website
Claude Desktop: download nexus-mcp.mcpb from the latest release and open it
Gemini CLI: gemini extensions install https://github.com/jaggernaut007/Nexus-MCP
Docker: docker run -i --rm -v "$PWD:/workspace" -v nexus-data:/data ghcr.io/jaggernaut007/nexus-mcp
GitHub Actions: uses: jaggernaut007/Nexus-MCP@v2

All channels, and what each needs: docs/DISTRIBUTION.md.

Claude Code plugin

The plugin registers the server and the same routing skill. It runs the server through uvx, so it needs only uv, no pip install:

/plugin marketplace add jaggernaut007/Nexus-MCP

/plugin install nexus-mcp@nexus-mcp

Use setup or the plugin for Claude Code, not both, or the server is registered twice. Tool names differ by route: mcp__nexus-mcp__search after setup, mcp__plugin_nexus-mcp_nexus-mcp__search through the plugin. The rest of this README writes bare names (search) and you do not need the prefix in prompts.

Optional: project instructions

If you want the rules in your own project file, a short block is enough:

## Code Navigation

The nexus-mcp server is connected. For questions about how this codebase works:

No setup call is needed; run index only if a tool says the project is not indexed.
Use search to find code, find_symbol for symbols, map for structure.
Use graph with transitive=true before you change a shared function. The call graph is static, so also use search for dynamic call sites.
Use plain grep or read when you already know the file or the exact string.

The full routing guide is in docs/AGENT_ROUTING.md.

How It Works

Indexing Pipeline (8 steps)

Source files

│ ├─ Step 1: Discover ──────── walk tree, filter by ext/size/.gitignore │ ├─ Step 2: Parse symbols ─── tree-sitter (parallel ThreadPool) │ extracts: functions, classes, methods │ captures: name, signature, docstring, line_start/end, language │ ├─ Step 3: Parse graph ────── ast-grep (sequential for consistency) │ extracts: containment edges, import edges, callee names of each function │ (indexing/call_resolver.py makes CALLS edges after the

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

Nexus-MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

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 Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Nexus-MCP

Results at a glance

The Problem It Solves

Evaluate on your project

Privacy and Data

Quickstart (60 seconds)

Claude Code plugin

Optional: project instructions

How It Works

Indexing Pipeline (8 steps)