mcp-stama: High-Performance Rust MCP Server for AI Agents

mcp-stama is a high-performance Model Context Protocol (MCP) server written in Rust, designed to provide AI coding agents with local context through a single static binary. It eliminates the overhead associated with Node.js or Python runtimes, reducing cold startup times from seconds to under 2 milliseconds and lowering memory usage from hundreds of megabytes to under 10MB.

Performance Benchmarks

mcp-stama significantly outperforms legacy MCP servers built on Node.js or Python across all primary resource metrics. By utilizing a native Rust implementation, it achieves near-instant execution and minimal system impact.

Metric	Legacy MCP Servers (Node / Python)	mcp-stama (Rust)
Cold Startup Time	1,500ms – 3,000ms	< 2ms
p50 Execution Latency	150ms – 800ms	300µs – 5ms
Memory Footprint (RSS)	180 MB – 350 MB	< 10 MB
Dependencies	100+ packages	Zero external runtimes
Installation	Requires Node.js / Python setup	Single static binary

Internal micro-benchmarks for specific tools further demonstrate this efficiency:

Tool Name	Invocations	p50 Latency	p99 Latency	Memory Footprint (RSS)
docker_watcher	100	327 µs	1.66 ms	< 10 MB
git_snapshot	100	462 µs	1.36 ms	< 10 MB
fast_grep	100	5.05 ms	8.72 ms	< 10 MB

Built-in High-Performance Tools

mcp-stama includes several specialized tools engineered to provide deep local context to AI agents without spawning heavy child processes or wasting prompt tokens.

fast_grep

This tool provides sub-millisecond file search and regex scanning. It utilizes ignore::WalkBuilder to automatically respect .gitignore and .ignore files while skipping binary and hidden files.

git_snapshot

Powered by gix (gitoxide), this is a pure-Rust Git inspector. It fetches HEAD commits, branch information, line deltas, and file status natively, removing the need to call the external git executable.

docker_watcher

This tool provides instant host and container diagnostics using bollard and sysinfo. It gives AI agents visibility into running Docker containers, system memory, CPU cores, and mapped ports.

Installation and Configuration

mcp-stama is distributed as a single static binary and includes an auto-configurator to streamline integration with AI editors.

Installation

• macOS / Linux: curl -fsSL https://raw.githubusercontent.com/StamManif/mcp-stama/main/install.sh | sh
• Windows (PowerShell): iwr -useb https://raw.githubusercontent.com/StamManif/mcp-stama/main/install.ps1 | iex
• Cargo: cargo install mcp-stama

Auto-Configuration

Users can automatically inject the server into their editor settings using the following flags:

• Claude Desktop: mcp-stama --install-claude
• Cursor: mcp-stama --install-cursor

Manual Configuration

For manual setup, add the following to the respective configuration files (~/.cursor/mcp.json for Cursor or claude_desktop_config.json for Claude Desktop):

{
  "mcpServers": {
    "mcp-stama": {
      "command": "mcp-stama",
      "args": []
    }
  }
}

Technical Architecture

mcp-stama implements a Pure Stdio JSON-RPC 2.0 interface. To maintain strict compliance with the JSON-RPC frame protocol, all diagnostic and logging outputs are routed to stderr, ensuring that stdout remains dedicated exclusively to protocol traffic.

The request flow follows a linear path: AI Client (Cursor/Claude) $\rightarrow$ StdioTransport $\rightarrow$ JsonRpcRequest/Response $\rightarrow$ ToolRegistry $\rightarrow$ Specific Tool (fast_grep, git_snapshot, or docker_watcher).