Model Context Protocol (MCP) Client
This Ruby MCP client allows AI assistants and services to discover, invoke, and manage external tools through a unified protocol, supporting multiple transport mechanisms and format conversions for AI API compatibility.
Added on:
Created by:
May 13 2025
This Ruby MCP client allows AI assistants and services to discover, invoke, and manage external tools through a unified protocol, supporting multiple transport mechanisms and format conversions for AI API compatibility.
Added on:
Created by:
May 13 2025
Szymon Kurcab
Featured
What is Model Context Protocol (MCP) Client?
The MCP Ruby client enables seamless integration of external tools with AI services by implementing the Model Context Protocol. It supports various communication transports, including stdio and SSE, and manages multiple MCP servers simultaneously. Users can discover tools, invoke actions, handle notifications, and convert tool formats for AI APIs like OpenAI and Anthropic. The client facilitates real-time streaming results, error handling, and custom RPC methods, making it suitable for complex AI workflows and tool management in Ruby environments.
Who will use Model Context Protocol (MCP) Client?
- AI developers
- Ruby developers integrating AI tools
- Organizations deploying AI assistants
- Researchers working with AI tool discovery
- Businesses automating workflows with external tools
How to use the Model Context Protocol (MCP) Client?
- Step 1: Install the gem via `gem install ruby-mcp-client` or add to Gemfile.
- Step 2: Configure MCP server connection (stdio or SSE) with server details.
- Step 3: List available tools using `list_tools`.
- Step 4: Find specific tools using `find_tools()` or `find_tool()`.
- Step 5: Invoke tools with `call_tool()` or `call_tools()` in batch.
- Step 6: Handle streamed results with `call_tool_streaming()`.
- Step 7: Convert tools for AI APIs (OpenAI, Anthropic) via provided methods.
- Step 8: Register for server notifications and handle responses.
- Step 9: Use `ping()` to verify server connectivity and `cleanup()` to close connections.
Model Context Protocol (MCP) Client's Core Features & Benefits
The Core Features
- List available MCP tools
- Invoke individual or batch tools
- Stream real-time tool results
- Convert formats for OpenAI, Anthropic, and Google APIs
- Support multiple transport mechanisms (stdio, SSE)
- Handle JSON-RPC notifications
- Configure servers via JSON files
- Support custom RPC methods
- Error handling and retries
- Server connectivity checks
The Benefits
- Facilitates easy integration with external AI tools
- Supports multiple communication transports for robustness
- Enables format compatibility across different AI APIs
- Provides real-time streaming results
- Supports batch tool invocation for efficiency
- Handles server notifications automatically
- Flexible server configuration via JSON
- Ensures thread-safe and reliable operation
- Simplifies complex AI workflow integration
- Open source and extendable
Model Context Protocol (MCP) Client's Main Use Cases & Applications
- Integrating external tools into AI chatbots for dynamic data retrieval
- Automating workflows that require external system access
- Supporting real-time streaming of tool results in AI applications
- Managing multiple external services with unified interface
- Converting tools for compatibility with different AI providers
FAQs of Model Context Protocol (MCP) Client
Developer
- simonx1
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