@friday-ai-codes/mcp
Related Servers
Alternatives to @friday-ai-codes/mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables AI coding agents to query a codebase as a knowledge graph, providing token-budgeted context, search, and impact analysis via MCP tools.MIT
- AlicenseNot gradedqualityCmaintenanceProvides 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.4Apache 2.0
- AlicenseNot gradedqualityBmaintenanceProvides 13 MCP tools for codebase analysis, wiki generation, and knowledge mapping, enabling AI assistants to understand project structure and context efficiently.2,798 npm1,415MIT
- AlicenseNot gradedqualityBmaintenanceEnables 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.396 npm7MIT
- AlicenseNot gradedqualityAmaintenanceExposes specialized agents as MCP tools so users can manage and triage Jira tasks, analyze repositories for health and conventions, generate commit/PR/DevOps patterns, assemble reports, produce slides and infographics, and run semantic search over a knowledge base. Everything is invokable directly from inside a coding session in Claude Code, Cursor, or Claude Desktop.1MIT
- FlicenseNot gradedqualityDmaintenanceProvides structural code intelligence via 26 MCP tools, enabling AI assistants to query code symbols, dependencies, and call graphs accurately without file-pasting.-
TDQS
Scored across 23 tools
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.
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.
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.
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.