Arcade MCP vs codebase-memory-mcp

Side-by-side comparison of two AI agent tools

Short answer

  • codebase-memory-mcp is growing faster: +1,725 GitHub stars in the last 30 days vs +33 for Arcade MCP.
  • Pick Arcade MCP for: the best way to create, deploy, and share MCP Servers. Pick codebase-memory-mcp for: mCP server indexing codebases into a persistent knowledge graph with tree-sitter and hybrid LSP.

From GitHub data refreshed daily.

Arcade MCPopen-source

The best way to create, deploy, and share MCP Servers

MCP server indexing codebases into a persistent knowledge graph with tree-sitter and hybrid LSP

Metrics

Arcade MCPcodebase-memory-mcp
Stars1.0k45.7k
Star velocity /mo33.3333333333333361.7k
Commits (90d)312.1k
Releases (6m)010
Overall score0.4780213456901140.906371149690189

Pros

  • +CLI-based project scaffolding with `arcade new` command streamlines server creation and setup
  • +Built on standardized MCP protocol ensuring compatibility with AI systems that support the standard
  • +Part of larger Arcade.dev ecosystem with prebuilt tools, examples, and comprehensive documentation

    Cons

    • -Requires understanding of MCP protocol concepts and Python development for effective use
    • -Relatively niche ecosystem compared to broader API integration approaches
    • -Limited to MCP-compatible AI systems and clients

      Use Cases

      • •Building custom tool servers to extend AI assistant capabilities with domain-specific APIs
      • •Creating reusable MCP servers for common integrations like databases, file systems, or web services
      • •Developing specialized AI tool ecosystems for enterprise or research environments

        FAQ

        Which is more popular, Arcade MCP or codebase-memory-mcp?
        codebase-memory-mcp has more GitHub stars (45,666 vs 1,044).
        Which is more actively developed, Arcade MCP or codebase-memory-mcp?
        codebase-memory-mcp had more commits in the last 90 days (2,093 vs 31).
        Should I use Arcade MCP or codebase-memory-mcp?
        Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.