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vision-opencode-mcp
by sddzwxy

vision-opencode-mcp

Give opencode (or any MCP client) visual reference by calling an OpenAI-compatible vision model API. Exposes one tool:

  • vision_describe(image_path?, question?) — read a local image and return what the vision model sees. When image_path is omitted, it automatically picks the newest image (by LastWriteTime) from the pasted-images dir, which is where opencode stores clipboard-pasted images.

Requires only python>=3.10 and the mcp SDK (installed automatically).

Requirements

  • Python 3.10+

  • A VISION_API_KEY for an OpenAI-compatible vision endpoint (base URL and model are baked in as defaults; override if needed)

Related MCP server: vision-mcp

Install

# from this repo
python -m venv .venv
# Windows:
.venv\Scripts\pip install -e .
# macOS/Linux:
# .venv/bin/pip install -e .

# or with uv (faster)
uv sync

This installs the vision-mcp console script and all dependencies (mcp, which pulls in mcp-types).

Configure for opencode

Add an mcp entry to your opencode config (opencode.json or ~/.config/opencode/opencode.json):

{
  "mcp": {
    "vision": {
      "type": "local",
      "command": [
        "C:\\path\\to\\vision-opencode-mcp\\.venv\\Scripts\\python.exe",
        "C:\\path\\to\\vision-opencode-mcp\\server.py"
      ],
      "enabled": true,
      "environment": {
        "VISION_API_KEY": "your-api-key-here"
      }
    }
  }
}

If you installed with uv sync, you can also point at the vision-mcp console script instead of the venv python:

{
  "mcp": {
    "vision": {
      "type": "local",
      "command": ["C:\\path\\to\\vision-opencode-mcp\\.venv\\Scripts\\vision-mcp.exe"],
      "enabled": true,
      "environment": {
        "VISION_API_KEY": "your-api-key-here"
      }
    }
  }
}

Restart opencode. The vision_describe tool then appears under the vision MCP server.

Environment variables

Variable

Default

Required

Description

VISION_API_KEY

(none)

yes

API key for the vision endpoint. Never hardcoded.

VISION_API_BASE

http://opencode.ai/zen/v1

no

Base URL of the OpenAI-compatible API.

VISION_MODEL

mimo-v2.5-free

no

Vision model name.

VISION_USER_AGENT

Chrome UA

no

Sent as User-Agent; some endpoints (Cloudflare-protected) return 403 without a browser UA.

VISION_TIMEOUT

180000

no

HTTP timeout in ms.

VISION_MAX_TOKENS

8192

no

max_tokens for the completion (thinking models need headroom).

VISION_PASTED_DIR

%TEMP%\opencode\pasted_images

no

Directory scanned for the newest image when image_path is empty.

Usage

With image_path:

call vision_describe  image_path="D:\pics\shot.png"  question="这个截图里报了什么错?"

Without image_path (auto-picks the newest pasted image):

call vision_describe  question="这张图片里有什么?"

CLI (debug)

The repo also ships vision.py, a small command-line version of the same call:

python vision.py <image_path> [question]

Prints the model text to stdout; exits 0 on success, 1 on error (prints ERROR: ...). It reads the same environment variables as the server.

Notes

  • The API key is read from the environment only — it never appears in the code, so this repo is safe to share.

  • For clipboard-pasted images in opencode desktop, the tool auto-picks the newest file under the pasted-images dir, so you can just say "看这张图" and the tool picks it up.

Available Tools

1 tool
vision_describeA

Give the model visual reference: read a local image file and return what a vision-capable model sees/understands about it. Use whenever you need to see or interpret an image (screenshots, diagrams, photos).

Inputs: question, image_path.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.1/5.0

    Scored across 1 tool

    Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly described and distinct by virtue of being the only option.

    Naming Consistency5/5

    The single tool name 'vision_describe' follows a consistent verb_noun pattern. With only one tool, internal naming consistency is trivially maintained.

    Tool Count3/5

    One tool is borderline thin for a server whose name suggests broader vision capabilities. However, the narrow focus on image description makes the count acceptable, if minimal.

    Completeness4/5

    The tool covers the core need of interpreting local image files, but lacks support for remote images or additional vision operations (e.g., OCR, image metadata). These are minor gaps for the stated purpose.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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