Opencode Openai Vision

A tiny MCP server that gives OpenCode (and any other MCP client) working image/vision support through any OpenAI-compatible endpoint — for example an OmniRoute or LiteLLM gateway, or OpenAI itself.

It reads an image file from disk, sends it to a vision-capable model as an image_url content block, and returns the model's text description.

Why this exists

OpenCode currently cannot send image attachments to vision models served through a custom OpenAI-compatible provider (@ai-sdk/openai-compatible). The attachment is dropped before it reaches the model, and the assistant replies with something like:

this model does not support image input

This is an OpenCode adapter bug, tracked upstream in anomalyco/opencode#20802 (fix PRs #26826 / #21627 were not merged at the time of writing). The underlying model and gateway are usually fine — a direct /chat/completions call with image_url works; only OpenCode's conversion is broken.

This project is a workaround: instead of relying on OpenCode's native image path, it routes images through an MCP tool that you fully control.

How it works

OpenCode (you paste a screenshot)
  └─ opencode-vision plugin            intercepts the image inside OpenCode,
     (separate project, see below)     saves it to a temp file, and rewrites the
                                        message to "call local_vision with this path"
        └─ local_vision (THIS server)  reads the file, base64-encodes it, and POSTs
                                        it as image_url to an OpenAI-compatible endpoint
              └─ your gateway/model     e.g. OmniRoute -> any vision model
                                        returns a text description back to OpenCode

This is a describe-then-reason approach: a vision model looks at the image and returns text; your main model works from that text. It is not native multimodality, but it restores the "paste a screenshot and ask" workflow.

Prerequisites

1. Node.js >= 18
2. A vision-capable model reachable via an OpenAI-compatible /chat/completions endpoint (OmniRoute, LiteLLM, OpenAI, etc.).
3. The opencode-vision plugin (AGPL-3.0) — this is what intercepts the pasted image inside OpenCode and calls the local_vision tool. This MCP server is the tool it calls. You need both.

Install

git clone https://github.com/WormAlien/opencode-openai-vision-mcp.git
cd opencode-openai-vision-mcp
npm install

Quick self-test (optional) — point it at your gateway and it should print a color word:

VISION_BASE_URL=http://localhost:20128/v1 \
VISION_API_KEY=your-key \
VISION_MODEL=your-vision-model \
node server.js
# then drive it with any MCP client, or just confirm it starts without error

Configure OpenCode

Add the MCP server to your opencode.json (see opencode.example.json):

{
  "mcp": {
    "local": {
      "type": "local",
      "command": ["node", "/absolute/path/to/opencode-openai-vision-mcp/server.js"],
      "enabled": true,
      "environment": {
        "VISION_BASE_URL": "http://localhost:20128/v1",
        "VISION_API_KEY": "YOUR_GATEWAY_API_KEY",
        "VISION_MODEL": "your-vision-model-or-alias"
      }
    }
  }
}

Naming the server local makes its tool resolve to local_vision, which matches the default imageAnalysisTool of the opencode-vision plugin — so no extra wiring needed.

Then enable the plugin for your models via opencode-vision.json (see opencode-vision.example.json):

{
  "models": ["your-provider/*"],
  "imageAnalysisTool": "local_vision"
}

Restart OpenCode, select a model under that provider, paste an image, and ask away.

Environment variables

Variable	Default	Description
VISION_BASE_URL	http://localhost:20128/v1	OpenAI-compatible base URL (must end in /v1).
VISION_API_KEY	(empty)	Bearer token for the endpoint. Omitted if empty.
VISION_MODEL	gpt-4o	Vision model name, or a gateway alias.
VISION_MAX_TOKENS	1024	Max tokens for the description.

Tip: point VISION_MODEL at a gateway alias (e.g. a vision alias in OmniRoute). Then you can swap the real model in your gateway dashboard without editing any config.

The vision tool

• Input: path (absolute path to a PNG/JPEG/WebP/GIF), optional question.
• Output: a text description (transcribes visible text by default).
• Handles both plain JSON and SSE/streamed responses from the endpoint.

Notes / limitations

• The image must be readable on the same machine the server runs on (it reads from the local filesystem path the plugin saved).
• Quality depends entirely on the vision model you point it at.
• Once OpenCode merges native image support for openai-compatible providers (#20802), you may not need this.

Credits

• opencode-vision (AGPL-3.0) — the OpenCode plugin that intercepts images and calls the MCP tool. A separate project; not bundled here.
• Model Context Protocol and its TypeScript SDK.

License

MIT — see LICENSE. (This applies to this MCP server only; the opencode-vision plugin has its own AGPL-3.0 license.)