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mario-andreschak

MCP Image Recognition Server

by mario-andreschak
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LOG_LEVELNoLogging level (DEBUG, INFO, WARNING, ERROR).
ENABLE_OCRNoEnable Tesseract OCR text extraction (true or false).
OPENAI_MODELNoOpenAI Model (default: gpt-4o-mini). Can use OpenRouter format for other models.gpt-4o-mini
TESSERACT_CMDNoOptional custom path to Tesseract executable.
OPENAI_API_KEYNoYour OpenAI API key.
OPENAI_TIMEOUTNoOptional custom timeout (in seconds) for the OpenAI API.
OPENAI_BASE_URLNoOptional custom base URL for the OpenAI API. Set to https://openrouter.ai/api/v1 for OpenRouter.
VISION_PROVIDERYesPrimary vision provider (anthropic, openai, or cloudflare).
CLOUDFLARE_MODELNoCloudflare Workers AI model (default: @cf/llava-hf/llava-1.5-7b-hf).@cf/llava-hf/llava-1.5-7b-hf
ANTHROPIC_API_KEYNoYour Anthropic API key.
FALLBACK_PROVIDERNoOptional fallback provider.
CLOUDFLARE_API_KEYNoYour Cloudflare API key.
CLOUDFLARE_TIMEOUTNoTimeout for Cloudflare API requests in seconds (default: 60).60
CLOUDFLARE_ACCOUNT_IDNoYour Cloudflare Account ID.
CLOUDFLARE_MAX_TOKENSNoMaximum number of tokens to generate (default: 512).512

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

NameDescription
describe_imageA

Describe an image from base64-encoded data. Use for images directly uploaded to chat.

Best for: Images uploaded to the current conversation where no public URL exists.
Not for: Local files on your computer or images with public URLs.

Args:
    image: Base64-encoded image data
    prompt: Optional prompt to guide the description

Returns:
    str: Detailed description of the image
describe_image_from_fileA

Describe an image from a local file path. Requires proper file system access.

Best for: Local files when the server has filesystem access to the path.
Limitations: When using Docker, requires volume mapping (-v flag) to access host files.
Not recommended for: Images uploaded to chat or images with public URLs.

Args:
    filepath: Absolute path to the image file
    prompt: Optional prompt to guide the description

Returns:
    str: Detailed description of the image
describe_image_from_urlA

Describe an image from a public URL. Most reliable method for web images.

Best for: Images with public URLs accessible from the internet.
Advantages: Works regardless of server deployment method (local/Docker).
Not for: Local files or images already uploaded to the current conversation.

Args:
    url: Direct URL to the image (must be publicly accessible)
    prompt: Optional prompt to guide the description

Returns:
    str: Detailed description of the image

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.7/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose based on the image source: base64-encoded data, local file path, or public URL. The descriptions explicitly state what each tool is 'best for' and 'not for,' eliminating any ambiguity about when to use which tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun_from_source pattern (describe_image, describe_image_from_file, describe_image_from_url). This predictable naming makes it easy for an agent to understand the relationship between the tools and their specific use cases.

Tool Count5/5

With 3 tools, this server is well-scoped for its purpose of image recognition across different input methods. Each tool earns its place by covering a distinct scenario (uploaded images, local files, web URLs), making the set complete without being bloated.

Completeness5/5

The tool surface comprehensively covers all common ways to provide an image for description: base64 data, local files, and public URLs. There are no obvious gaps, as these three methods handle most practical use cases an agent would encounter in image recognition tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources