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.