ontomics

Name: ontomics
Author: EtienneChollet

by EtienneCholletversion 0.3.1i★ –iRuns on your computer

Registry nameio.github.EtienneChollet/ontomics

Install

Overview

Description from the maker (original English)

Index your codebase's domain knowledge. 20x fewer tokens, 10x faster.

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).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx 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": {
    "ontomics": {
      "command": "npx",
      "args": [
        "-y",
        "@ontomics/ontomics"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ontomics -- npx -y @ontomics/ontomics

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ontomics": {
      "command": "npx",
      "args": [
        "-y",
        "@ontomics/ontomics"
      ]
    }
  }
}

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": {
    "ontomics": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@ontomics/ontomics"
      ]
    }
  }
}

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account EtienneChollet, 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 7259 servers in this category
Published to the registry more recently than 9% of the other 7259 servers in this category
Basic checks equal to or better than 100% of the other 7259 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 ontomics 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 (0.3.1) reached the registry 7 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 on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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

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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

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Codebase Memory

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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

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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.

ontomics

ontomics gives any coding agent instant knowledge of your codebase. One tool call instead of 19. ~20x fewer tokens.

https://github.com/user-attachments/assets/01afa8a0-1bc2-4686-94d7-965fef7610c3

Visualization for the voxelmorph project -- a library for unsupervised learning in image registration

Benchmark

Tested with Claude Sonnet — same question, with and without ontomics.

"What does 'transform' mean in this codebase?" on voxelmorph (full transcript):

	With ontomics	Without
Tool calls	1	19
Tokens	~3.7k	~76k
Time	5s	1m 15s
Answer quality	Complete	Complete

"What are the main domain concepts in this codebase?" on ScribblePrompt (full transcript):

	With ontomics	Without
Tool calls	1	26
Tokens	~3.7k	~61.6k
Time	~5s	56s
Answer quality	Complete	Complete

Both conditions produced complete, correct answers. ontomics got there in one call.

What it does that search can't

Search tells you where a string appears. An LSP tells you where a symbol is defined and referenced. Neither answers: what are the domain concepts in this codebase? How do they relate? What naming conventions emerged? What changed in the domain vocabulary since last release? Which functions behave similarly, regardless of what they're named?

ontomics builds a semantic index of your project's domain — clustering related symbols into concepts, detecting naming conventions from usage frequency, resolving abbreviations, grouping functions by behavioral similarity, and tracking how the vocabulary evolves over time. That index can be exported as a portable artifact to bootstrap conventions in other repos.

Behavioral similarity

Beyond naming and concepts, ontomics embeds raw function bodies using CodeRankEmbed (768-dim, contrastive code retrieval) and clusters them by behavioral similarity. This surfaces relationships that neither naming nor call graphs expose:

❯ What functions behave like spatial_transform()?

random_transform() nn/functional.py:352 0.80 spatial_transform() functional.py:596 0.69 random_transform() functional.py:1399 0.67 random_disp() nn/functional.py:275 0.65 integrate_disp() functional.py:764 0.65 compose() nn/functional.py:216 0.63 disp_to_trf() functional.py:343 0.62

The result also reveals that random_transform appears at two locations with different similarity scores — a sign of implementation duplication that concept-level search would miss entirely.

Install

Install once, available in every project. No configuration needed — ontomics auto-detects the repo and indexes it on first run.

ontomics requires a git repository (.git/ directory). It will refuse to index home, root, or temp directories. To index a non-git directory, pass --force.

1. Install the binary

npm (macOS/Linux):

npm install -g @ontomics/ontomics

macOS (Homebrew):

brew install EtienneChollet/tap/ontomics

Shell installer (macOS/Linux):

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/EtienneChollet/ontomics/releases/latest/download/ontomics-installer.sh | sh

From source:

git clone https://github.com/EtienneChollet/ontomics.git

cd ontomics cargo build --release

2. Register with your harness

Claude Code:

claude mcp add -s user ontomics -- ontomics

Codex:

codex mcp add ontomics -- ontomics

OpenClaw:

openclaw mcp set ontomics '{"command":"ontomics"}'

pi-coding-agent:

pi install npm:@ontomics/ontomics

Share with your team — drop an .mcp.json in your repo root:

"mcpServers": { "ontomics": { "command": "npx", "args": ["-y", "@ontomics/ontomics", "--repo", "."] } } }

Supported languages

Python, TypeScript, JavaScript, Rust. Auto-detected from file extensions.

Tools

Concepts and vocabulary

Tool	What it does
`query_concept`	Find all variants, related concepts, and occurrences of a term
`locate_concept`	Find the key signatures, classes, and files for a concept
`describe_symbol`	Get the signature, docstring, and relationships for a function or class
`trace_concept`	Trace how a concept flows through the codebase via call chains
`list_concepts`	List the top domain concepts by frequency
`list_conventions`	List all detected naming patterns (prefixes, suffixes, conversions)
`list_entities`	List code entities (classes, functions) filtered by concept, role, or kind
`check_naming`	Check an identifier against project conventions; suggests the canonical form
`suggest_name`	Generate an identifier name that fits the project's vocabulary
`vocabulary_health`	Measure convention coverage, naming consistency, and cluster cohesion
`ontology_diff`	Show new, changed, or removed domain concepts since a git ref
`export_domain_pack`	Export domain knowledge as portable YAML for use in other repos

Behavioral similarity

Tool	What it does
`find_similar_logic`	Find functions with behaviorally similar implementations, ranked by embedding similarity
`describe_logic`	Get the behavioral description, body text, and logic cluster membership for a function
`compact_context`	Assemble tiered context (concepts + logic) for a symbol, optimized for LLM consumption

Codebase structure

Tool	What it does
`describe_file`	Overview of a file's entities, concepts, and relationships
`concept_map`	Show which modules contain which domain concepts
`type_flows`	Show dominant types and how data flows through the codebase
`trace_type`	Trace how a specific type propagates across files and call sites

Resources

Resource	What it does
`ontomics://briefing`	Session briefing: top conventions, abbreviations, key concepts, contrastive pairs, and vocabulary warnings. Also available via `ontomics briefing` CLI.

How it works

ontomics runs a multi-stage pipeline entirely on your machine — no API keys required:

Parse — tree-sitter extracts every identifier, signature, and call site from your source files
Analyze — TF-IDF scoring identifies domain-specific concepts and detects naming conventions
Embed (concepts) — BGE-small (384-dim) clusters related concepts by semantic similarity
Embed (logic) — CodeRankEmbed (768-dim) embeds raw function bodies and clusters them by behavioral similarity
Centrality — PageRank scores entities by structural importance

Both embedding models are downloaded once on first run and cached locally. The index lives at /.ontomics/index.db — subsequent startups load from cache and watch for file changes.

Configuration via .ontomics/config.toml in the repo root. All fields have sensible defaults. See SPEC.md for the full design contract.

ontomics

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 Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

ontomics

Benchmark

What it does that search can't

Behavioral similarity

Install

1. Install the binary

2. Register with your harness

Supported languages

Tools

Concepts and vocabulary

Behavioral similarity

Codebase structure

Resources

How it works