"WEMO" matching MCP servers:

A demonstration implementation of the Model Context Protocol server that facilitates communication between AI models and external tools while maintaining context awareness.

A simplified Model Context Protocol server in Python that reads text input and generates humorous responses, with support for Docker and Azure deployment.

Provides persistent, client-side-encrypted memory for AI coding assistants via SAIHM, enabling tools to remember, recall, and forget with provable erasure.

A demonstration project that uses Cloudflare Workers MCP to create custom AI tools that can be integrated with Claude, Cursor, and other AI assistants supporting the Model Context Protocol.

Provides non-custodial, post-quantum encrypted memory for AI agents, enabling portable memory across models with provable erasure.

Serves Agent Skills over MCP using the SEP-2640 Skills Extension, enabling skills discovery, retrieval, and resource reading via MCP.

Enables stateless human-in-the-loop DevOps automation, demonstrating MCP SEP-2322 multi-round trip requests for emergency database migration authorization.

Provides an MCP server that gives LLM agents an evolutionary memory: entries pay upkeep and survive only if they earn their keep through measured outcomes, with tools for querying, settling, and managing memory.

Enables natural-language analysis of insurance customer migration data through CrewAI agents, providing tools for querying renewals, churn, competitor returns, and feature adoption.

A self-aware, AI-powered MCP server that acts as a personal medical education assistant for African students, understanding natural language and autonomously managing reminders, data bundles, and medical knowledge retrieval.

Enables remote MCP client connections with WorkOS AuthKit authentication, supporting organization-centric user management and permission-based tool access control.

Enables interaction with the Agent Legion platform through MCP. Provides tools such as tool_say_hi to demo the server's capabilities.

Enables AI agents to safely mutate business state by demonstrating phase-gating, validation-before-mutation, and structured audit logging in a toy inventory and purchase order system.

Enables AI assistants to interact with a commercial management system through controlled capabilities for querying orders, analyzing invoices, summarizing customer activity, and creating orders, with role-based access control and audit trails.

Provides MCP tools to enforce spend policies (allow, deny, step-up, allowlist) on agent wallets with an immutable audit trail.

A hands-on demonstration project that teaches the Model Context Protocol (MCP) through Python code, allowing users to understand how AI models interact with their context through a provider-agent architecture.

Remote MCP/API trust gate for AI agents to inspect readiness before performing Korean business workflow operations like vendor onboarding and payment release.

Demonstrates how to implement sampling in MCP servers, allowing tools to request LLM content generation from the client without requiring external API integrations or credentials.