ontomics

Name: ontomics
Author: EtienneChollet

作者：EtienneChollet版本 0.3.1i★ –i在你的电脑上运行

注册名称io.github.EtienneChollet/ontomics

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 EtienneChollet 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7264 个服务器的 0%
发布到注册中心的时间比此分类中其他 7264 个服务器的 9% 更新
基础检查结果等于或优于此分类中其他 7264 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

ontomics 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.3.1）于7个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

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

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

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

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

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