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friday-ai-codes

@friday-ai-codes/mcp

by friday-ai-codes
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    Related Servers

    • OpenCodeHub MCP Server

      laithalsaadoon
      A
      license
      Not graded
      quality
      C
      maintenance
      Provides code intelligence for AI coding agents by indexing repositories into a hybrid knowledge graph, enabling agents to query dependencies, impact, and context through 28 MCP tools.
      4
      Apache 2.0

    • codesight

      Houseofmvps
      A
      license
      Not graded
      quality
      C
      maintenance
      Provides 13 MCP tools for codebase analysis, wiki generation, and knowledge mapping, enabling AI assistants to understand project structure and context efficiently.
      1,995 npm
      1,422
      MIT

    • CodeGraph

      Phoenixrr2113
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to search code by meaning, explore codebase structure, store and query knowledge with temporal facts, and read source code through a set of MCP tools.
      290 npm
      7
      MIT

    • Vibe Coder MCP Server

      jkaramanlis76530
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to perform software development tasks such as codebase analysis, research, planning, requirements and user-story generation, task management, project scaffolding, and multi-step workflow execution. It integrates with MCP-compatible clients like Claude Desktop, Cursor, and Cline AI.
      22 npm
      MIT
    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A4.1/5.0

    Scored across 23 tools

    Disambiguation5/5

    All tools have clearly distinct purposes with detailed descriptions. Even similar tools like grep_repository and search_rag_chunks are explicitly differentiated (exact text vs semantic search). Overlap is minimal and intentional.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (e.g., analyze_repository, create_coding_plan, search_rag_chunks). No mixing of camelCase or other conventions.

    Tool Count4/5

    23 tools is slightly above the ideal range but well-justified given the comprehensive domain coverage (code analysis, planning, execution, knowledge management). No obvious bloat.

    Completeness4/5

    The tool surface covers the full lifecycle from repository discovery to code execution and knowledge persistence. Minor gaps exist (e.g., no direct file editing or issue/PR commenting outside Feishu), but core workflows are complete.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources