MCP server for extracting and converting images to base64 for LLM analysis
MCP Image Extractor becomes available to MCP clients through the mcp image extractor mcp server. MCP server for extracting and converting images to base64 for LLM analysis.
MCP server for extracting and converting images to base64 for LLM analysis.
Once connected, the assistant can call these 3 tools directly:
extract_image_from_file — Extracts an image from a local file and converts it to base64extract_image_from_url — The extract_image_from_url tool exposed by this serverextract_image_from_base64 — Parameters: - base64 (required): Base64-encoded image data - mime_type (optional, default: "image/png"): MIME type of the imageThe server is distributed via npm as directly, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. MCP Image Extractor sits in that group, and the shape of its toolset — extract_image_from_file, extract_image_from_url, extract_image_from_base64 — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| extract_image_from_file | Extracts an image from a local file and converts it to base64. |
| extract_image_from_url | The extract_image_from_url tool exposed by this server. |
| extract_image_from_base64 | Parameters: - base64 (required): Base64-encoded image data - mime_type (optional, default: "image/png"): MIME type of the image |
{
"mcpServers": {
"image-extractor": {
"command": "npx",
"args": [
"-y",
"mcp-image-extractor"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.