LLM Agent from an Image

by fffilonico.huggingface.fffiloni/llm-from-image

Get a LLM Assistant personality idea from an image

  • Remote endpoint
  • 1 tool

Uptime

Checked  · every day

100%from 4 days of checks

Up without downtime on 4 of 4 days with data.

  • No downtime
  • Degraded
  • Down over 5 minutes
  • No data
  • Not checked yet

Response time

Median 1139 ms, slowest 5% 1268 ms for the handshake and the tool list, over 30 days.

1500 ms750 ms0

How far each check got

  • Reached the address 100%
  • Completed the handshake 100%
  • Listed its tools 100%

1 tool at the last answer.

Every day
DayResultChecksMedian
3 Nov (today)No downtime11210 ms
2 NovNo downtime1of 11201 ms
1 NovNo downtime1of 11198 ms
31 OctNo downtime11064 ms

A failure counts as down time only when a second check five minutes later fails too. How we check

Connect to the publisher's own endpoint

Run in a terminal:

claude mcp add --transport sse llm-from-image https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse
For agents: the install prompt

Every step above as one Markdown text, for an assistant installing it for you.

# Install the MCP server LLM Agent from an Image (`co.huggingface.fffiloni/llm-from-image`)

Its page: https://plug.directory/servers/co.huggingface.fffiloni/llm-from-image

It runs on the publisher's servers; the client connects to its URL.

Use the section for the person's client.

## Claude Desktop

Paste into Settings › Connectors › Add custom connector:

```text
https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse
```

## Claude Code

Run in a terminal:

```sh
claude mcp add --transport sse llm-from-image https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse
```

## Cursor, Devin Desktop, LM Studio, Gemini CLI, oterm, ECA

Merge into the place for the person's client:

- Cursor: ~/.cursor/mcp.json
- Devin Desktop: ~/.config/devin/mcp_config.json
- LM Studio: mcp.json (Program › Install › Edit mcp.json)
- Gemini CLI: ~/.gemini/settings.json
- oterm: config.json in oterm's data folder (~/Library/Application Support/oterm on macOS, ~/.local/share/oterm on Linux, %APPDATA%\oterm on Windows)
- ECA: ~/.config/eca/config.json (global) or .eca/config.json in the project

```json
{
  "mcpServers": {
    "llm-from-image": {
      "url": "https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

## VS Code

Merge into .vscode/mcp.json:

```json
{
  "servers": {
    "llm-from-image": {
      "type": "sse",
      "url": "https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

## Zed

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Cline, AnythingLLM, Augment Code

Merge into the place for the person's client:

- Cline: cline_mcp_settings.json (MCP Servers › Configure › Configure MCP Servers)
- AnythingLLM: anythingllm_mcp_servers.json in the AnythingLLM storage plugins directory
- Augment Code: Augment Settings Panel – MCP section – Import from JSON (VS Code extension)

```json
{
  "mcpServers": {
    "llm-from-image": {
      "type": "sse",
      "url": "https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

## Continue

Merge into ~/.continue/config.yaml:

```yaml
mcpServers:
  - name: "llm-from-image"
    type: sse
    url: "https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse"
```

## Goose

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Codex

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## mcp-client-cli, TUUI, BoltAI, Bob Shell, Kiro, Kiro CLI

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Agenvoy

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Amp

Merge into ~/.config/amp/settings.json (user) or .amp/settings.json (workspace):

```json
{
  "amp.mcpServers": {
    "llm-from-image": {
      "url": "https://fffiloni-llm-from-image.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

Then restart the client, or reload its MCP servers, and ask it to list the server's tools to confirm it started.

Tools 1

  • llm_from_image_infer Generate a system prompt idea for a language model based on the content of an input image. This function performs two steps: 1. It uses a vision-language model (Kosmos-2) to generate a descriptive caption of the input image. 2. It then uses a text generation pipeline (Zephyr-7B) to create a chatbot configuration from that caption, including a title, system prompt, and example user message. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool. Returns: Tuple[str, str]:, - The generated caption describing the image., - A suggested LLM system prompt structure including:, - A chatbot title, - A system message defining the bot’s personality, - An example user input message

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