praxis-mcp 0.2.0

Model Context Protocol (MCP) client for AI agent tool execution
Documentation

praxis-mcp

Model Context Protocol (MCP) client for AI agent tool execution.

Overview

This crate provides a high-level interface for connecting to MCP servers and executing tools within AI agent workflows.

Features

  • Connect to multiple MCP servers simultaneously
  • Execute tools with structured arguments
  • Full async/await support
  • Error handling and timeouts
  • Built on rmcp (Rust MCP SDK)

Installation

[dependencies]
praxis-mcp = "0.1"

Usage

Basic Tool Execution

use praxis_mcp::{MCPExecutor, MCPConfig};

// Create executor with multiple servers
let executor = MCPExecutor::new(vec![
    MCPConfig {
        name: "weather".to_string(),
        url: "http://localhost:8005/mcp".to_string(),
    }
]).await?;

// Execute tool
let result = executor.execute_tool(
    "get_weather",
    serde_json::json!({"location": "San Francisco"})
).await?;

println!("Result: {}", result);

Multi-Server Setup

use praxis_mcp::{MCPExecutor, MCPConfig};

let executor = MCPExecutor::new(vec![
    MCPConfig {
        name: "weather".to_string(),
        url: "http://localhost:8005/mcp".to_string(),
    },
    MCPConfig {
        name: "search".to_string(),
        url: "http://localhost:8006/mcp".to_string(),
    },
]).await?;

// Executor automatically routes to the correct server
let weather = executor.execute_tool("get_weather", args).await?;
let search = executor.execute_tool("web_search", args).await?;

List Available Tools

let tools = executor.list_tools().await?;

for tool in tools {
    println!("Tool: {} - {}", tool.name, tool.description);
}

MCP Protocol

The Model Context Protocol (MCP) is a standard for connecting AI agents to external tools and data sources. This crate implements the client side of the protocol.

Key concepts:

  • Server: Hosts one or more tools
  • Tool: A function that can be called with structured arguments
  • Resource: Data that can be read (future support)

Examples

See the examples/ directory for complete examples:

# Run the simple MCP agent example
cargo run --example simple_mcp_agent

Part of Praxis Framework

This crate is part of the Praxis AI Agent Framework:

License

MIT