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Sunex Optics MCP Server

Sunex Optics MCP Server

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by Sunex-AI
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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.

Related MCP server: Agent Construct

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.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Resources

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