How do I install Optics?

Optics is a remote plugin. You do not need to install anything locally. Add the server URL to your AI app's MCP configuration and you are ready to go.

What AI apps work with Optics?

Optics uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Optics MCP Server

by Sunex Ai

Developer ToolsModerate7.4MCP RegistryRemote

Free

Server data from the Official MCP Registry

Search Sunex lenses and imager sensors, match compatible optics, get pricing. No API key needed.

About

Search Sunex lenses and imager sensors, match compatible optics, get pricing. No API key needed.

Remote endpoints: sse: https://mcp.sunex-ai.com/sse

Security Report

7.4

Moderate7.4Low Risk

Valid MCP server (1 strong, 1 medium validity signals). 5 known CVEs in dependencies Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.

Endpoint verified · Open access · 6 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

file_system

Check that this permission is expected for this type of plugin.

HTTP Network Access

Connects to external APIs or services over the internet.

network_websocket

Check that this permission is expected for this type of plugin.

How to Connect

Remote Plugin

No local installation needed. Your AI client connects to the remote endpoint directly.

Add this to your MCP configuration to connect:

{
  "mcpServers": {
    "com-sunex-optics-mcp": {
      "url": "https://mcp.sunex-ai.com/sse"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

Sunex Optics MCP Server

A public Model Context Protocol server that lets AI assistants search Sunex's lens and imager catalog in natural language.

Live endpoint: https://mcp.sunex-ai.com/mcp Landing page: sunex-ai.com Transport: Streamable HTTP (MCP spec 2025-03-26). Legacy SSE endpoint at /sse preserved for older clients.

Connect in 30 seconds

Claude

Settings → Connectors → Add custom connector → paste https://mcp.sunex-ai.com/mcp

Cursor / Continue / Zed

Add to your MCP config with transport streamable-http and the URL above.

ChatGPT

Via any MCP → OpenAPI bridge as a custom GPT Action.

Five tools

Tool	What it does
`recommend_lens_for_imager`	Give it an imager PN → compatible lenses with FOV and angular resolution. One shot.
`search_imagers`	Find sensors by PN, manufacturer, or resolution class.
`get_imager_detail`	Full sensor specs plus computed geometry (width / height / diagonal in mm).
`find_compatible_lenses`	Given pixel count + pitch, return lenses whose image circle covers the sensor.
`search_products`	Full catalog search by PN or keyword, with sample pricing and RFQ links.

Example prompts

"Recommend a wide-angle lens for the Sony IMX577 with F/2.0 or faster."
"I need fisheye lenses under $100."
"What's the diagonal of the IMX477 in mm?"
"Find lenses for a 1920×1080 sensor with 3µm pixels, 100–180° HFOV."

Architecture

Claude / Cursor / ChatGPT  →  mcp.sunex-ai.com  →  optics-online.com/api/v1
     (MCP client)         (Cloudflare Worker)      (ASP JSON API)

Thin proxy on Cloudflare Workers (free tier) over Sunex's production catalog. Streamable HTTP transport per MCP spec 2025-03-26 (with legacy SSE preserved). No auth, read-only.

Endpoints

Path	Purpose
`/mcp`	Primary — Streamable HTTP transport (current MCP standard)
`/sse`	Legacy SSE transport, preserved for backward compatibility
`/.well-known/mcp.json`	Public discovery manifest
`/`	Landing page with install instructions

Self-host

git clone https://github.com/Sunex-AI/Optics-mcp
cd Optics-mcp
npm install
npx wrangler login
npx wrangler deploy

Calling a tool directly (Python)

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

async with streamablehttp_client("https://mcp.sunex-ai.com/mcp") as (r, w, _):
    async with ClientSession(r, w) as session:
        await session.initialize()
        result = await session.call_tool(
            "recommend_lens_for_imager",
            {"imagerPn": "IMX577", "fNumMax": 2.0}
        )

Discovery

Public manifest: https://mcp.sunex-ai.com/.well-known/mcp.json

Contributing

Issues and PRs welcome. For requests about the backend API (pricing, additional catalog fields, new endpoints), email support@sunex.com.

License

MIT — see LICENSE.

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Optics MCP Server

About

Security Report

Findings (6)

Permissions Required

How to Connect

Documentation

Sunex Optics MCP Server

Connect in 30 seconds

Claude

Cursor / Continue / Zed

ChatGPT

Five tools

Example prompts

Architecture

Endpoints

Self-host

Calling a tool directly (Python)

Discovery

Contributing

License

Reviews

No reviews yet

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