Skip to main content
Glama
Sign In
fm-mcp

fm-mcp

by yrangana
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Rust
Local

Related Servers

Alternatives to fm-mcp

No user-submitted related servers found.

    Related Servers

    • lucid-apple-mcp

      Lucid-Systems-LLC
      A
      license
      Not graded
      quality
      C
      maintenance
      MCP server that gives Claude and local LLMs access to Apple's on-device frameworks — Vision OCR, NSDataDetector, and Apple Intelligence FoundationModels. Everything runs on your Mac with zero data leaving.
      1
      MIT

    • apple-fm-mcp

      yihan2099
      A
      license
      Not graded
      quality
      D
      maintenance
      MCP server that enables local Apple on-device Foundation Model access via any MCP client, supporting text generation, structured output, and multi-turn chat on macOS.
      2
      MIT
    • ToolPiper

      ToolPiperofficial

      ModelPiper
      A
      license
      Not graded
      quality
      A
      maintenance
      426+ MCP tools for macOS, all on-device — local AI inference (llama.cpp on Metal), voice, vision OCR, local RAG, browser automation, and ~140 system actions across 26 macOS domains. Nothing leaves your Mac.
      3
      MIT

    • macos-vision-mcp

      woladi
      A
      license
      A
      quality
      A
      maintenance
      Local OCR & image analysis via Apple Vision Framework — private, offline, no API keys. Extracts text from images and PDFs, detects faces, barcodes, QR codes, and document corners. Works with Claude Code, Claude Desktop, and Cursor.
      6
      97 npm
      8
      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

    A4.5/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool targets a clearly distinct operation: classify (pick from given labels), extract (schema-bound fields), ocr (read text from images), and summarise (condense prose). There is no overlap in verb, input type, or output shape, and each description even states what it is NOT for. An agent can select unambiguously.

    Naming Consistency4/5

    Three tools are lowercase verbs (classify, extract, summarise) in a consistent style with no noun collisions. The single deviation is 'ocr', an acronym/process name rather than a verb, but it is a standard, unambiguous term and the overall set still reads as a predictable pattern.

    Tool Count5/5

    Four tools is well-scoped for a focused on-device text-processing server. Each tool covers a genuinely different capability (classification, extraction, OCR, summarisation) and none feels redundant or extraneous.

    Completeness4/5

    The surface covers the main on-device operations: classify, extract, ocr, and summarise, with clear input/output behavior for each. Minor gaps exist, such as no explicit translation, Q&A, or generic generation tool, but the core text-processing lifecycle is well covered and workable.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing

    Resources