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
c
codebase-memory-mcpopen-source
MCP server indexing codebases into a persistent knowledge graph with tree-sitter and hybrid LSP
Metrics
| Arcade MCP | codebase-memory-mcp | |
|---|---|---|
| Stars | 1.0k | 45.7k |
| Star velocity /mo | 33.333333333333336 | 1.7k |
| Commits (90d) | 31 | 2.1k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.478021345690114 | 0.906371149690189 |
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.