Skip to main content
Glama
QinLuza

Umi-OCR MCP Server

by QinLuza
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to Umi-OCR MCP Server

No user-submitted related servers found.

    Related Servers

    • Kimi Vision MCP Server

      kira4094
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables analysis of local images through Kimi (Moonshot AI) vision models via the MCP protocol, supporting features like OCR and long context understanding.
      37 npm
      MIT

    • mimo-vision-mcp

      wenren93
      A
      license
      Not graded
      quality
      B
      maintenance
      A local MCP server that exposes Xiaomi's MiMo-V2.5 vision model as an inspect_image tool, enabling AI agents to analyze images with structured JSON output including OCR, visual evidence, and normalized coordinates.
      15 npm
      MIT

    • Vision MCP

      Amengclass
      A
      license
      A
      quality
      B
      maintenance
      Enables any MCP client to perform image understanding and OCR via any OpenAI-compatible vision-language model. Supports local, private inference without images leaving the machine.
      2
      13 npm
      MIT
    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

    A3.9/5.0

    Scored across 7 tools

    Disambiguation4/5

    Tools are mostly distinct: OCR methods target different input types (file, base64, PDF, batch, directory). The two status-check tools serve different granularities (detailed vs quick), but their overlap could cause slight confusion despite clear descriptions.

    Naming Consistency2/5

    Naming is inconsistent: some tools use 'ocr_' prefix (ocr_batch, ocr_directory), others use different patterns (check_ocr_status, quick_ocr_status, extract_text_umi_v2). The 'extract_text_umi_v2' name includes a version suffix, breaking convention.

    Tool Count5/5

    With 7 tools, the set is well-scoped for an OCR server. Each tool has a clear role: status checks, single-image OCR from various sources, batch, and directory scanning. No unnecessary tools.

    Completeness4/5

    Covers the main OCR workflow: status check, single image from file/base64/PDF, batch, and directory. Minor gaps like multi-page PDF OCR or clipboard input are absent but not critical for the core use case.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources