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PopNx MCPMCP servers for people who use AI · explained in plain words

Codeix

by montanetechversion 0.5.0i★ –iRuns on your computer
Registry nameio.github.montanetech/codeix

Install

Overview

Description from the maker (original English)

Fast semantic code search for AI agents — find symbols, references, and callers across any codebase

PopNx MCP’s three pillars

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

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).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

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": {
    "codeix": {
      "command": "npx",
      "args": [
        "-y",
        "codeix"
      ]
    }
  }
}

Run this in your terminal

claude mcp add codeix -- npx -y codeix

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "codeix": {
      "command": "npx",
      "args": [
        "-y",
        "codeix"
      ]
    }
  }
}

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": {
    "codeix": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "codeix"
      ]
    }
  }
}

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

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account montanetech, 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 7226 servers in this category
  • Published to the registry more recently than 3% of the other 7226 servers in this category
  • Basic checks equal to or better than 100% of the other 7226 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 Codeix 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 (0.5.0) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, 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 3 days ago
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 20 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 44 days ago
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.

codeix

[codeix.dev](https://codeix.dev) · Fast semantic code search for AI agents — find symbols, references, and callers across any codebase.

codeix                 # start MCP server, watch for changes

codeix build # parse source files, write .codeindex codeix -r ~/project build # build from a specific directory

Why

AI coding agents spend most of their token budget *finding* code before they can *work on* it. They grep, read files, grep again, backtrack. On a large codebase the agent might burn thousands of tokens just locating the right function — or worse, miss it entirely and hallucinate.

Codeix gives the agent a pre-built map of your codebase. One structured query returns the symbol name, file, line range, signature, and parent — no scanning, no guessing.

What existing tools get wrong
ProblemWhat happens today
No structuregrep finds text matches, not symbols. The agent can't distinguish a function definition from a comment mentioning it.
Slow re-parsingPython-based indexers re-parse everything on startup. On large codebases, you wait.
Not shareableIndexes are local caches — ephemeral, per-machine. A new developer or CI runner starts from scratch.
No compositionMonorepo with 10 packages? Dependencies with useful APIs? No way to query across boundaries.
Prose is invisibleTODOs, docstrings, error messages — searchable by grep but not *selectively*. You can't search only comments without also matching code.
What codeix does differently
  • Committed to git — the index is a .codeindex directory you commit with your code. Clone the repo, the index is already there. No re-indexing.
  • Shareable — library authors can ship .codeindex in their npm/PyPI/crates.io package. Consumers get instant navigation of dependencies.
  • Composable — the MCP server auto-discovers dependency indexes and mounts them. Query your code and your dependencies in one place.
  • Structured for LLMs — symbols have kinds, signatures, parent relationships, and line ranges. The agent gets exactly what it needs in one tool call instead of piecing it together from raw text.
  • Prose search — search --scope text targets comments, docstrings, and string literals specifically. Find TODOs, find the error message a user reported, find what a function's docstring says — without noise from code.
  • Fast — builds in seconds, queries in milliseconds. Rust + tree-sitter + in-memory SQLite FTS5 under the hood.

The .codeindex format

An open, portable format for structured code indexing. Plain JSONL files you commit alongside your code — git-friendly diffs, human-readable with grep and jq, no binary blobs.

.codeindex/

index.json # manifest: version, name, languages files.jsonl # one line per source file (path, lang, hash, line count) symbols.jsonl # one line per symbol (functions, classes, imports, with signatures) texts.jsonl # one line per comment, docstring, string literal

Any tool that can parse JSON can consume a .codeindex. Codeix builds it using tree-sitter, and AI agents query it through MCP (Model Context Protocol).

Example — symbols.jsonl:

{"file":"src/main.py","name":"os","kind":"import","line":[1,1]}

{"file":"src/main.py","name":"Config","kind":"class","line":[22,45]} {"file":"src/main.py","name":"Config.init","kind":"method","line":[23,30],"parent":"Config","sig":"def init(self, path: str, debug: bool = False)"} {"file":"src/main.py","name":"main","kind":"function","line":[48,60],"sig":"def main(args: list[str]) -> int"}

Ship your index with your package

Include .codeindex in your package and every developer who depends on you gets instant navigation of your API — no setup, no re-indexing.

Works with Git repos, npm, PyPI, and crates.io.

MCP tools

Seven tools, zero setup. The agent queries immediately — no init, no config, no refresh.

ToolWhat it does
exploreExplore project structure: metadata, subprojects, files grouped by directory
searchUnified full-text search across symbols, files, and texts (FTS5, BM25-ranked) with scope/kind/path/project filters
get_file_symbolsList all symbols in a file
get_childrenGet children of a class/module
get_callersFind all places that call or reference a symbol
get_calleesFind all symbols that a function/method calls
flush_indexFlush pending index changes to disk

Project discovery

Launch codeix from any directory. It walks downward and treats every directory containing .git/ as a separate project — each gets its own .codeindex.

Works uniformly for single repos, monorepos, sibling repos, and git submodules. No config needed.

Languages

Tree-sitter grammars, feature-gated at compile time:

LanguageFeature flagDefaultExtensions
Pythonlang-pythonyes.py .pyi .pyw
Rustlang-rustyes.rs
JavaScriptlang-javascriptyes.js .mjs .cjs .jsx
TypeScriptlang-typescriptyes.ts .mts .cts .tsx
Golang-goyes.go
Javalang-javayes.java
Clang-cyes.c .h
C++lang-cppyes.cpp .cc .cxx .hpp .hxx
Rubylang-rubyyes.rb .rake .gemspec
C#lang-csharpyes.cs
Markdownlang-markdownyes.md .markdown
Markdown support

Markdown files are parsed for headings (both ATX # and Setext underline styles) which are indexed as section symbols with hierarchical parent-child relationships — enabling TOC extraction and document structure navigation.

Fenced code blocks are extracted as code text entries, parented to their containing section.

Embedded scripts

HTML, Vue, Svelte, and Astro files are preprocessed to extract embedded blocks, which are then parsed with the JavaScript or TypeScript grammar:

FormatExtensionsScript detection
HTML.html .htm tags, with optional lang="ts"
Vue.vue and , with optional lang="ts"
Svelte.svelte , with optional lang="ts"
Astro.astro--- frontmatter (always TypeScript) + optional tags

Line numbers in the index point to the original file, not the extracted script block.

Install

# npm / npx — run without installing

npx codeix

pip / uvx — run without installing

uvx codeix

Rust

cargo install codeix

Homebrew

brew install codeix

Or build from source

git clone https://github.com/montanetech/codeix.git cd codeix cargo build --release

All channels install the same single binary. No runtime dependencies.

Usage

# Build the index for the current project

codeix build

Build from a specific directory (discovers all git repos below)

codeix -r ~/projects build

Start MCP server (default command, watches for changes)

codeix

Or explicitly

codeix serve codeix serve --no-watch

Serve from a specific directory

codeix -r ~/projects serve

MCP client configuration

Add to your MCP client config (e.g. Claude Desktop, Cursor):

{

"mcpServers": { "codeix": { "command": "codeix" } } }

Design principles

  • Local only — no network, no API keys, works offline and air-gapped
  • Deterministic — same source always produces the same index (clean diffs)
  • Composable — dependency indexes are auto-discovered and mounted at query time
  • Minimal surface — 7 query tools, zero management plumbing

Architecture

See docs/architecture.md for the full set of architecture decision records.

License

MIT OR Apache-2.0

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