Using watsonx.ai Flows Engine with Model Context Protocol (MCP)
by royderks
What is Using watsonx.ai Flows Engine with Model Context Protocol (MCP)
watsonx.ai Flows Engine
Build, run & deploy Tools for AI Agents 🚀
With watsonx.ai Flows Engine you can build tools out of any data source, and deploy them to an endpoint in the cloud. Tools built with watsonx.ai Flows Engine can be used in any Agentic Framework using the SDK for Python & JavaScript.
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Tools
Integrations
Examples
- End-to-end Agent Chat App
- Text-to-SQL Agent
- YouTube transciption agent
- Math agent
- Model Context Protocol (MCP)
- Tool Calling
- LangGraph
- LangChain
- watsonx.ai
- OpenAI
- RAG
- Summarization
Support
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Frequently Asked Questions
What is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.
What are MCP Servers?
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
How do MCP Servers work?
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Are MCP Servers secure?
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
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