Skip to main content
Glama
ducnm9

ollama-vision-mcp

by ducnm9
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to ollama-vision-mcp

No user-submitted related servers found.

    Related Servers

    • local-vision-mcp

      tmchao7
      A
      license
      Not graded
      quality
      B
      maintenance
      MCP server for local Ollama vision analysis, enabling text-only agents like Claude Code to inspect images via a single tool. Processes images locally with Ollama, keeping image bytes on the machine and returning text reports.
      2
      MIT

    • llm-vision-mcp

      ghbalf
      A
      license
      Not graded
      quality
      C
      maintenance
      An MCP server that enables any LLM to describe images from file paths, URLs, or base64 data by forwarding them to a supported vision provider such as OpenAI, Anthropic, or local Ollama models.
      969 npm
      10
      MIT

    • llm-vision

      maple-red
      A
      license
      Not graded
      quality
      C
      maintenance
      MCP server that provides a 'borrowed eye' for text-only LLMs, enabling them to identify and describe local images via the Qwen VL vision model, including face recognition, scene description, OCR, and targeted visual questioning.
      2 npm
      Apache 2.0
    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

    A3.6/5.0

    Scored across 4 tools

    Disambiguation3/5

    describe_image and ask_image both handle arbitrary image analysis, and process_clipboard_image explicitly supports describe/ocr/custom-question tasks, creating overlap. However, the input-source distinction (file vs clipboard) reduces ambiguity, and ocr_image is clearly distinct.

    Naming Consistency4/5

    Three tools follow a consistent verb_noun pattern (describe_image, ocr_image, ask_image), and process_clipboard_image also uses verb_noun but adds a modifier. The naming is mostly consistent and predictable.

    Tool Count4/5

    4 tools is a reasonable size for a vision-focused server, covering core operations without being bloated. It could have been higher if not for the redundancy between clipboard and file-based tools.

    Completeness4/5

    The toolset covers the main vision tasks (description, OCR, custom Q&A) and handles both file and clipboard inputs. Minor gaps like batch processing or image comparison are not essential for the stated purpose.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

    Resources