Skip to main content
Glama
sachinshelke

Codevira MCP

by sachinshelke
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to Codevira MCP

No user-submitted related servers found.

    Related Servers

    • Tages

      ryantlee25-droid
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to maintain persistent, cross-session memory of codebase architecture, naming conventions, and decisions through MCP tools. Eliminates repetitive project re-explanation by automatically injecting stored context into every session with local-first SQLite storage and optional team sharing capabilities.
      4
      MIT

    • Claude Habitat

      Ydy4HYW7ExA
      F
      license
      Not graded
      quality
      Not graded
      maintenance
      An MCP server that provides persistent project context, workflow management, and knowledge capture for AI coding agents. It enables agents to maintain structured memory across sessions by tracking project profiles, conventions, skills, and technical debt.
      7
      -

    • blueocean-vector

      thammarongg
      A
      license
      B
      quality
      B
      maintenance
      A shared, persistent MCP memory server for coding agents that enables storing and retrieving project decisions and context across different tools like Claude Code, Codex, and Cursor using semantic vector search.
      8
      MIT

    • toon-memory

      LuiggiVal08
      A
      license
      Not graded
      quality
      A
      maintenance
      MCP memory server for AI coding agents to remember decisions, patterns, and bugs between sessions. Provides persistent memory with 37 MCP tools, multi-session coordination, and token-efficient recall.
      86 npm
      11
      MIT

    • promem-mcp

      DIP-RO
      A
      license
      B
      quality
      A
      maintenance
      Enables AI coding agents to maintain persistent project context, including rules, decisions, environment intelligence, and Git history, using a local-first MCP server with automatic project detection and token-efficient retrieval.
      13
      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 36 tools

    Disambiguation5/5

    Each tool targets a distinct operation and resource: roadmap phases, decision records, working memory, skills, and graph queries are cleanly separated. Even related tools like search_decisions vs list_decisions and get_node vs query_graph are clearly differentiated by their descriptions and intended use cases.

    Naming Consistency4/5

    The overwhelming majority of tools follow snake_case verb_noun naming like add_phase, get_roadmap, search_decisions, and record_skill. Minor deviations such as working_add, working_get, expand, and origin_of break the pattern slightly but are still readable and not chaotic.

    Tool Count2/5

    36 tools is well beyond the 25+ threshold that should be considered too many for a coherent MCP surface. While the tools cover multiple subdomains, the sheer number makes the set heavy for an agent to navigate and weigh in context.

    Completeness5/5

    The tool surface provides deep lifecycle coverage for its apparent domain: roadmap phases, decision records, skills, working memory, and file context graph. There are few obvious missing operations — decisions can be recorded, searched, superseded, flagged, and outdated; skills can be created, listed, promoted, and managed; and phases have create, read, update, complete, and bulk-import operations.

    Maintenance

    ActivityActive
    ResponsivenessResponsive

    Resources