"Understanding AWS EC2 Pricing" matching MCP servers:

A Model Context Protocol server allowing Claude AI to interact with AWS resources through natural language, enabling users to query and manage AWS services without using the traditional AWS Console or CLI.

Enables users to analyze data from BitFlux-enabled AWS EC2 instances and AWS pricing APIs to determine optimal instance types for their workloads. It provides recommendations by comparing instance usage statistics with current AWS pricing and resource offerings.

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with AWS S3 Control API through natural language, auto-generated using AG2's MCP builder.

A custom server with tools that enable AI agents to provision and terminate AWS EC2 instances through natural language commands.

Enables AI agents to discover, evaluate, and provision cloud infrastructure across AWS, GCP, and Azure with cross-cloud normalization, cost comparisons, and deployable execution kits.

MCP server that delegates Claude Code subagents to alternative backends such as local models, DeepSeek, or AWS Bedrock, while keeping your Claude Code orchestrator session intact.

Compute price oracle for AI agents. Compare inference pricing across OpenAI, Anthropic, and DePIN providers like Hyperbolic. Get routing recommendations that save up to 80% on compute costs.

MCP Compass is a discovery and recommendation service that assists AI assistants in finding and understanding Model Context Protocol servers through natural language queries.

Provides unified access to 100+ AI models from OpenAI, Anthropic, Google, AWS Bedrock and other providers through a single MCP interface. Enables seamless switching between different AI models using LiteLM's unified API without requiring separate integrations for each provider.

A Model Context Protocol server that enables Claude Desktop to play the classic Lemonade Stand business simulation game with dynamic weather, pricing strategies, and inventory management.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.

Enables AI assistants to spawn and manage long-running cloud agents via MCP, offloading tasks to AWS Bedrock AgentCore.

Bridges AI IDEs with AWS Bedrock AgentCore Gateway using Cognito OAuth and hardware-bound token encryption for secure, authenticated access to dynamically discovered tools.

Enables selecting the best AI model and reasoning effort for any task by analyzing task requirements and live pricing, balancing intelligence, speed, and cost. Provides model recommendations, comparisons, and routing status through MCP tools.

Discovers LLM models in real time from cloud providers and local Ollama instances, returning compatibility profiles and live pricing so AI agents can route tasks to the cheapest viable model without breaking tool calls or context clipping.

Unified Model Context Protocol server that bundles Slack, GitHub, Jira, PostgreSQL, Redis, AWS, webhooks, and email into one lightweight Docker container, providing a single REST API and web dashboard for AI agent integration.

A search engine for AI agents that enables searching, comparing, and discovering over 500 businesses across 112 categories with structured pricing data. It allows users to filter listings by price, category, and country to find and analyze specific business services.

Universal MCP router and gateway that bridges LLM agents to OpenAPI, GraphQL, and AWS Lambda services with ISO/IEC 42001 AI governance, RBAC, PII redaction, semantic tool routing, and a web dashboard.

Enables management and invocation of AWS Bedrock managed prompts with support for variable substitution, streaming responses, batch processing, and multiple AI models including Claude, Titan, Llama, and Mistral.

An MCP server that acts as a governance proxy for AI agents, evaluating each tool call against policies before execution, enabling secure and controlled access to systems like Slack, GitHub, and AWS without exposing credentials to the agent.