About gyazo-mcp-server
gyazo-mcp-server is a TypeScript MCP server for accessing Gyazo images. It lets AI assistants search, retrieve, and upload captures, with image content, metadata, and available OCR exposed through MCP.
Key features
- 01Search captures by text and filters
- 02Retrieve images by ID or URL
- 03Access image content and metadata
- 04Read OCR text when available
- 05Upload images with optional metadata
Use cases
- 01Find a capture by keyword or date range
- 02Retrieve an image and its details for an AI workflow
- 03Upload an image and receive its Gyazo link
MCP support
Exposes Gyazo images as MCP resources and provides tools for searching, fetching, retrieving the latest image, and uploading. Image resources include content and metadata, with OCR when available.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEgyazo-mcp-server
A Model Context Protocol server for Gyazo image integration
This is a TypeScript-based MCP server that provides access to Gyazo images. It allows AI assistants to access and interact with Gyazo images through the Model Context Protocol, providing:
- Resources representing Gyazo images with URIs and metadata
- Tools for searching, fetching, and uploading images
- Image content and metadata access via the Gyazo API
Features
Resources
- List and access Gyazo images via
gyazo-mcp://URIs - Each image includes:
- Original image content
- Metadata (title, description, app, URL)
- OCR data (if available)
- Supports various image formats (JPEG, PNG, etc.)
Tools
-
gyazo_search- Full-text search for captures uploaded by users on Gyazo- Search by keyword, title, app, URL, or date range
- Supports pagination for browsing multiple results
- Returns matching image URIs and metadata
-
gyazo_image- Fetch image content and metadata from Gyazo- Retrieve specific images by ID or URL
- Returns both image content and detailed metadata
-
gyazo_latest_image- Fetch the most recent image from Gyazo- Returns both image content and metadata
- Includes OCR text if available
-
gyazo_upload- Upload an image to Gyazo- Upload images with base64 encoded image data
- Add optional metadata like title, description, referer URL, and app name
- Returns the uploaded image's permalink URL and ID
Installation
NPM Package
The easiest way to install the Gyazo MCP server is via npm:
npm install -g @notainc/gyazo-mcp-server
Prerequisites
- Create a Gyazo account if you don't have one: https://gyazo.com
- Get your Gyazo API access token from: https://gyazo.com/api
- Click "Register applications" button
- Click "New Application" button
- Fill in the form with your app name and description
- Name and Callback URL are required
- You can use
http://localhostfor the Callback URL
- Click "Submit" button
- Click application name to view details
- Scroll down to "Your Access Token"
- Click "Generate" button
- Copy "Your access token" value
- Set the
GYAZO_ACCESS_TOKENenvironment variable with your token
Claude Desktop Integration
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Using NPM package (recommended)
{
"mcpServers": {
"gyazo-mcp-server": {
"command": "npx",
"args": ["@notainc/gyazo-mcp-server"],
"env": {
"GYAZO_ACCESS_TOKEN": "your-access-token-here"
}
}
}
}
Using Docker (optional)
{
"mcpServers": {
"gyazo-mcp-server": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GYAZO_ACCESS_TOKEN",
"gyazo-mcp-server"
],
"env": {
"GYAZO_ACCESS_TOKEN": "your-access-token-here"
}
}
}
}
Development
Install dependencies:
npm ci
Build the server:
npm run build
For development with auto-rebuild:
npm run watch
Docker Build (optional)
npm run image:build
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.