"Amazon Prime" matching MCP servers:

Enables a fresh Claude Code session to retrieve the repository's versioned convention card — grounding triggers, calibration rules, and tool map — over stdio via get_conventions and list_scopes. It is read-only, leaves CLAUDE.md unmodified, and requires no network service or third-party runtime.

A Model Context Protocol (MCP) server that enables integration of Strands agents with Amazon Q and other MCP-compatible systems through a plugin architecture.

Enables AI assistants to request human input by creating tasks on Amazon Mechanical Turk, integrating human-in-the-loop capabilities via the MCP standard.

Enables MCP clients to compute shortest paths, topological dependency orderings, maximum network flows, minimum spanning trees, and PageRank-style centrality over graph topologies using only the Python standard library. It supports optimal multi-agent communication backbone and execution-plan analysis without external graph dependencies.

Exposes foldkit 7-prime spine, κ-band classification, and 6 fold operations as tools and resources for MCP-compatible AI clients like Claude Code.

Enables users to achieve high-level life and commerce goals by decomposing them into autonomous steps, such as planning trips, searching Prime-eligible products, optimizing a budget cart, and authorizing purchases through human-in-the-loop confirmation.

Enables Alexa+ to monitor budgets, check affordability, recommend Amazon deals within spending limits, and execute automated purchase plans through MCP tools.

Exposes foldkit's 7-prime spine, 7 κ-bands, and 6 fold operations as MCP tools and resources for AI agents to fold/unfold states, classify utterances, and apply fold operations.

A Model Context Protocol server that integrates with Amazon Braket, allowing AI assistants to access, control, and interpret results from quantum computing resources.

A Model Context Protocol server that enables AI assistants to create images and videos using Amazon Nova Canvas and Nova Reel models.

A containerized MCP server deployed on Amazon EKS that provides a simple calculator tool for adding two numbers. Demonstrates how to deploy and scale MCP servers in a Kubernetes environment with proper session management and HTTP endpoints.

Open-source MCP server for controlling Autodesk Fusion 360 from any AI agent, enabling 3D modeling operations via natural language.

Enables users to analyze, save, compare, and track bounty opportunities through a stateful MCP workflow, build submission plans, and request the next best action across sessions.

Enables users and autonomous agents to calculate dynamic price elasticity of demand and identify marginal revenue maxima through a zero-dependency, MCP-compatible Python tool.

Enables a simulated assistant to investigate synthetic incidents, request containment actions that require separate human reauthentication and approval, apply authorized effects, and verify durable signed receipts while rejecting tampering and replay.

Enables approval-gated actions for Alexa+ by exposing MCP tools that plan changes, require explicit user approval, execute only after precondition checks, and verify results with SHA-256 evidence digests.

Enables tracking post-purchase prices across Shopify, Walmart, Best Buy, and Target, detecting drops within merchant price-match windows, verifying eligibility, and synthesizing automated refund claims.

Enables Alexa+ to collect recent security and IoT context, reason locally, prepare bounded physical actions for explicit approval, execute them through adapters, and return verified evidence in real time.

Enables a self-hosted AI workspace with persistent sessions, agent run/step execution logs, unified search across conversations and knowledge, and discovery/invocation of local and remote MCP tools.

An MCP server that enables Alexa+ to autonomously orchestrate multi-step household tasks by decomposing goals into parallel tool calls — cross-session memory and dietary/allergy auditing, culinary planning with wine pairings, Amazon Fresh cart assembly with human-in-the-loop purchase approval, smart-home ambiance and climate control, and calendar scheduling. It pairs AWS Bedrock multi-agent reasoning with Streamable HTTP transport and interactive generative UI cards, carousels, and live IoT sliders.

Enables users to register post-purchase receipts, monitor retailer price drops within price-match guarantee windows, verify eligibility, and synthesize automated refund claims.

Enables autonomous agents to calculate dynamic price elasticity of demand and derive marginal revenue maxima from query inputs. It returns structured JSON outputs with execution telemetry for economic modeling and pricing optimization.