Skip to main content
Glama
yh008

AI App MCP

by yh008

Related Servers

Alternatives to AI App MCP

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      A comprehensive Python MCP server with built-in knowledge base (SQLite + FTS5), web management interface, and flexible tool grouping system. Supports multiple transport protocols (stdio, SSE, HTTP Stream) with zero external dependencies.
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      A production-ready foundation for building secure, observable MCP servers with built-in authentication, rate limiting, and reference tools like database-query and semantic-search.
      15
      11 npm
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      A foundational template for building MCP servers in Python using Streamable HTTP transport. Provides example implementations of tools, resources, and prompts to help developers create custom MCP integrations for AI assistants.
      -

    TDQS

    B3.4/5.0

    Scored across 5 tools

    Disambiguation4/5

    All tools have distinct roles: search and get_document are clearly separate (search vs. reading full doc), build_rag_prompt and normalize_user_query serve different pipeline stages, and health_check is standalone. Slight potential confusion exists between search_knowledge_base and get_document, but descriptions resolve this.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: search_knowledge_base, get_document, build_rag_prompt, normalize_user_query, health_check. The pattern is predictable and readable.

    Tool Count5/5

    Five tools is a well-scoped count for a RAG-focused server. Each tool serves a clear purpose in the pipeline, and the count feels neither too thin nor overly heavy.

    Completeness4/5

    The set covers the core RAG flow (normalize, search, retrieve, build prompt) plus health check. Missing operations like adding/updating knowledge base documents or a combined retrieve tool, but these are minor gaps for the apparent scope.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues