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Enables interaction with Kubernetes clusters through Model Control Protocol (MCP) tools.
Enables analysis and visualization of AWS cloud spending data using Anthropic's Claude model as an interactive interface.
Enables operations on AWS resources, including S3 and DynamoDB, through the Model Context Protocol.
Enables Large Language Models to inspect BigQuery database schemas and execute queries.
Provides a standardized interface to interact with and manage Kubernetes clusters.
Enables AI assistants to interact with Confluent Cloud REST APIs for managing Kafka topics, connectors, and Flink SQL statements.
Enables AI agents to interact with the Terraform Registry API for provider, resource, and module information.
Demonstrates how to enhance ChatBot applications using Amazon Bedrock with the Model Context Protocol (MCP) to enable seamless integration with external systems and data sources.
Deploys a custom remote server to the cloud using Azure Functions for use with the Model Context Protocol.
Enables Large Language Models to execute AWS Lambda functions as tools via Anthropic's Model Control Protocol (MCP) without code modifications.
Deploys a custom remote Model Context Protocol (MCP) server to the cloud using Azure Functions with Python.
Enables AI assistants to interact with and manage Azure Kubernetes Service (AKS) clusters.
Enables MCP clients to remember information about users across conversations, leveraging vector search for relevant memory retrieval.
Enables interaction with NetBox data via Large Language Models (LLMs) using the Model Context Protocol (MCP).
Enables database introspection, management, and interaction with self-hosted Supabase instances from development environments using the Model Context Protocol.
Dynamically creates and manages Model Context Protocol (MCP) servers as child processes, enabling a flexible MCP ecosystem.
Provides example implementations for building agentic AI solutions with AWS, utilizing the Model Context Protocol.
Transforms SAP S/4HANA and ECC OData services into dynamic conversational AI interfaces by exposing them as MCP tools.
Manages Railway account resources and automates workflows through a local Model Context Protocol server.
Exposes Portainer environment data through the Model Context Protocol (MCP), enabling AI assistants to interact with containerized infrastructure.
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