Skip to main content
Glama

Related Servers

Alternatives to gemini-faf-mcp

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      A
      maintenance
      Persistent project context in Rust. 8 MCP tools via rmcp SDK — parse, validate, score, compress, discover, and token analysis. Single binary, zero config. IANA-registered format (application/vnd.faf+yaml). One file, every AI platform.
      90 npm
      4
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Persistent project context MCP server that syncs a single .faf file to all AI tool formats (Cursor, Windsurf, Cline, etc.), enabling eternal bi-sync and optimized context for AI assistants.
      15
      217 npm
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Standardize Context, Not Intelligence. An open protocol and native MCP server for preserving, organizing, and serving structured project context (architecture, decision logs, domain rules, and roadmap) to AI coding assistants (Cursor, Claude Desktop, Antigravity) with built-in CLI verification
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Smart documentation generator and intelligent versioning system with full MCP support, enabling AI assistants like Claude and Gemini to manage project snapshots, generate docs, and control versioning via 26 MCP tools.
      31 PyPI
      1
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Portable, auditable, local-first MCP memory for MCP-compatible AI agents and coding workflows. It keeps durable project memory outside the model runtime, compresses continuity into smaller working packs, and carries forward operational state so agents can resume with less repetition.
      28
      39
      Apache 2.0

    TDQS

    A3.9/5.0

    Scored across 13 tools

    Disambiguation4/5

    Most tools target distinct operations (read, discover, init, migrate, export), and the descriptions actively differentiate the two closest pairs: faf_validate vs faf_score (error details vs quick status) and faf_read vs faf_context (full structure vs AI-optimized subset). The overlap is real but the guidance reduces misselection, leaving only mild ambiguity.

    Naming Consistency5/5

    Every tool uses a uniform faf_ prefix followed by a single snake_case noun/verb (faf_read, faf_score, faf_discover, faf_migrate). The pattern is fully predictable throughout with no mixed conventions.

    Tool Count5/5

    13 tools sit squarely in the well-scoped 3-15 range, and each maps to a concrete lifecycle step (discover, init/auto, read/context, validate/score, stringify, migrate, export). No tool feels redundant or padded.

    Completeness4/5

    The surface covers the full FAF lifecycle: discovery, creation (init/auto), reading (read/context), validation/scoring, serialization, migration, and two export targets (GEMINI.md, AGENTS.md). Minor gaps exist — no explicit delete/edit for arbitrary slots and no updater beyond faf_auto filling empty slots — but core workflows are covered.

    Maintenance

    ActivityActive
    ResponsivenessNo issues