MCP Image Extractor
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| extract_image_from_fileA | Extract and analyze images from local file paths. Supports visual content understanding, OCR text extraction, and object recognition for screenshots, photos, diagrams, and documents. |
| extract_image_from_urlA | Extract and analyze images from web URLs. Perfect for analyzing web screenshots, online photos, diagrams, or any image accessible via HTTP/HTTPS for visual content analysis and text extraction. |
| extract_image_from_base64A | Extract and analyze images from base64-encoded data. Ideal for processing screenshots from clipboard, dynamically generated images, or images embedded in applications without requiring file system access. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose based on the source of the image: base64-encoded data, local file paths, and web URLs. The descriptions reinforce this by specifying different use cases (e.g., clipboard screenshots, local files, online images), leaving no ambiguity for an agent to misselect.
All tool names follow a consistent verb_noun pattern with 'extract_image_from_' as a prefix, followed by the source type (base64, file, url). This predictable naming scheme makes it easy for agents to understand and navigate the tool set without confusion.
With 3 tools, the server is well-scoped for its purpose of extracting images from different sources. Each tool earns its place by covering a distinct input method (base64, file, URL), providing a complete set for the domain without being overly sparse or bloated.
The tool surface is complete for the domain of image extraction, covering all major input sources: base64 data, local files, and web URLs. There are no obvious gaps, as these three methods encompass the typical ways images are accessed in applications, ensuring agents can handle various scenarios without dead ends.