"Information about network devices" matching MCP servers:

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

MCP server that enables Claude Code to communicate with other Claude Code agents over HTTP, allowing users to ask questions about remote codebases or delegate coding tasks.

Enables agents to check a public hostname's declared agent policy, official API or developer path, provider status, and recent observations, returning a recommended action for blocked web steps.

Enables AI agents to discover, pay for, and retrieve receipts for external APIs via USDC micropayments through the SynapseNetwork Gateway, supporting stdio and Remote MCP.

Enables Claude Code sessions to discover each other as named peers and exchange instant messages across directories, machines, and Docker containers, with durable delivery for offline sessions.

Enables agents to compute shortest paths, topological orderings, maximum flow and minimum cut bottlenecks, minimum spanning trees, and centrality rankings for graph-based multi-agent routing. It runs as a zero-dependency MCP server using standard Python libraries.

MCP server exposing specialized AI agents (CEO, CTO, Designer, Sales, Marketing, QA) that collaborate through an intelligent orchestrator for business strategy, technology, design, sales, marketing, and quality tasks.

A TypeScript server implementing the Model Context Protocol that enables AI agents to interact with the Akash Network, allowing them to deploy applications, create leases, manage deployments, and access other Akash services through typed tools.

Enables interaction with Phala Network's confidential computing infrastructure, including TEE worker management, AI agent deployment in secure enclaves, and blockchain operations.

Enables local coding agents to track sessions, exchange isolated collaboration messages, and optionally delegate coding work to Codex with Git worktree isolation and task management.

Enables MCP clients like Claude Desktop to interact with ANP (Agent Network Protocol) agents through three core tools: authentication setup, document fetching, and OpenRPC method invocation. Converts ANP's crawler-style interaction paradigm into MCP-compatible tools for seamless agent communication.

MCP server that lets AI agents dispatch physical tasks to robot executors and track the task -> proof -> verify -> settle workflow, enabling task creation, executor discovery, proof submission, and verification status checks.

Enables AI assistants to query cross-organizational best practices, learnings, publishers, and patterns from the OTP network.

Provides AI assistants with real-time access to the AGNO framework documentation by enabling them to browse, search, and fetch documentation pages. This server allows users to query information about AGNO's Agents, Teams, and Workflows directly through MCP-compatible clients.

AI social network and marketplace for agentic tools and services. With 6 MCP tools available at launch.

MCP server for OpenClaw that enables AI clients to control smart home devices, manage memory and cron jobs, and orchestrate multiple AI sub-agents.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

A server that enables interaction with Home Assistant devices and automations through the Model Context Protocol, allowing users to monitor device states, control devices, trigger automations, and list entities.

Provides tools for AI assistants to interact with smart home devices through Home Assistant, allowing operations like checking entity states and calling services.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.