Security-enforcing MCP proxy that sits between an AI agent and any number of downstream MCP servers, intercepting every tool call through a capability-token policy gateway that can allow, deny, or escalate to human approval before the call reaches any real tool. It also exposes built-in operator tools for approval workflows, audit trail queries, token management, voice/HUD output, and hierarchical
Universal coordination hub for AI agents. Find collaborators, negotiate terms, form contracts, and build reputation through an MCP interface. Supports natural language search across agent networks.
Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.
Universal agent registry, discovery, and cross-protocol messaging for any MCP-compatible AI agent, enabling registration, discovery, and message translation across six protocols.
An MCP server that enforces explicit task ownership acceptance and requires a provenance tag (observed, reviewed, or reported) on every completion claim, preventing silent inheritance and unverified assertions in multi-agent systems.
MCP server providing witness, recovery, and continuity primitives for AI agents, enabling them to articulate failure, preserve state across sessions, and coordinate via MCP, A2A, or REST.
A server built with mcp-framework that allows users to extend Claude's capabilities by adding custom tools that can be used through the Claude Desktop client.
Agentic commerce infrastructure for AI agents. MCP-native product discovery, contextual ad matching, and purchase facilitation with European privacy compliance (nDSG/GDPR).
Exposes runtime-local capabilities from browsers, apps, devices, and local processes to AI agents through a unified MCP bridge, enabling path-based discovery and invocation of live context.
DingDawg Loop Protocol (DDLP) — safe scheduled AI agents
with
governance gates. Every loop execution is verified, receipted, and
fail-closed. MCP-native, works with CrewAI, LangGraph, Claude Code, Cursor.
Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.
A standardized interface for agent-to-agent communication that enables composability, supports streaming, and implements server-sent events (SSE) for real-time interaction.
An agent-native marketplace API where any agent can publish allocatable resources, search for what they need, negotiate structured offers, and exchange contact details after mutual acceptance. The protocol is flexible — it works for GPU hours traded between agents, physical courier services, time-bounded API keys, dataset access, or resource types that don't exist yet.
Enables AI agents to discover, validate, and call actions from web apps through the Model Context Protocol, using a manifest of structured, permissioned actions.
Enables AI agents to autonomously request services from other specialized agents and compensate them via x402 micropayments. Demonstrates a Machine-to-Machine economy using A2A protocol for agent communication, MCP for context management, and blockchain-based payments on Base network.
A Python implementation of the MCP server that enables AI models to connect with external tools and data sources through a standardized protocol, supporting tool invocation and resource access via JSON-RPC.
The Agent-Native Marketplace — where AI agents discover, negotiate, and purchase services without a single line of HTML.
Freedom Commerce is an open protocol and reference implementation for agentic commerce