"Finding Information About Public Places" matching MCP servers:

Public MCP front door for Spala agent discovery, providing onboarding, documentation search, and template/addon listings for planning backend workflows.

Enables coding agents to consult, design, implement, and review with an independent AI peer via Oh My Pi, using persistent project-isolated conversations and structured MCP tools for thinking, analysis, and authorized work.

Exposes an MCP toolset that routes each coding task to the cheapest capable model, from a free local LLM up to Claude, while giving clients file, shell, git, web, memory, knowledge-search and parallel swarm-delegation capabilities. Also allows an OpenAI-compatible REST and WebSocket API to be served for non-MCP clients.

Enables agents to drive a headless Chrome browser that is already logged in to the user's accounts, supporting multiple accounts and safe handling of Google sessions without manual login or 2FA.

Ontology-driven multi-agent platform that encodes business policy as code, enabling deterministic decision-making and multi-domain agent collaboration without code redeploy.

A multi-domain multi-agent system served over MCP (Model Context Protocol) with FastMCP, exposing specialized domain agents as tools to automate business workflows.

Bridges Claude Code and Codex TUI in real time, allowing them to share the same thread and send messages back and forth.

Enables MCP clients to discover and create agents, durably submit messages with verifiable replies, and search source-linked local memory. Exposes eight authenticated tools with owner-approved OAuth, transcript-backed verification, and explicit control over history import and embedding.

Enables AI agents to request purchase approval from humans, receive scoped virtual cards, complete checkout, and report receipts for audit.

Enables Claude agents to report their working, waiting, done and error status, ask questions you answer in the office, hand work off to colleagues, and get an overview or end-of-day summary of who is doing what per project.

Enables agents to spawn, monitor, and control CLI coding agents running in isolated tmux sessions, with verified input injection, redacted pane capture, fleet and per-session state classification, lineage tracking, blocking exec with exit codes, cross-agent messaging, waste analytics, and log/ledger reading. Ownership checks, policy controls, and audit events keep every control action safe by default.

Enables independent Poke instances to discover each other's capabilities and exchange cryptographically signed, consented, idempotent messages and data through MCP tools, resources, and prompts. Provides handshake, message sending, data exchange, receipt retrieval, and capability listing with signature verification, policy enforcement, and signed error envelopes.

Multi-server MCP aggregator with 266 skills, an orchestration runtime, fleet/claims coordination, and hook-driven session governance for autonomous Claude/Cursor/Gemini agent runs.

An MCP server that enables an assistant to operate a self-hosted HAPI environment by discovering machines and agents, selecting models and permissions, managing sessions, sending prompts, inspecting evidence, running native terminal commands, working with files, and coordinating task graphs.

Give anything on your machine a public HTTPS link — live from your computer, or hosted by serve.

Enables MCP clients to drive a sandboxed Chromium container over CDP with tools for navigation, clicking, typing, selecting, key presses, scrolling, finding, text extraction, snapshots, and screenshots, optionally restricted by a host allowlist.

Cross-device communication between AI agents over the public internet

Enables AI agents to choose the lowest-carbon cloud region for a workload, with tools for ranking regions by carbon intensity and latency, and for finding optimal start times to reduce emissions.

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.

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

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

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.

A public-safe research prototype for controlling AI-agent tool actions with deterministic policy, risk-based human approval, time-bound authorization and a tamper-evident audit chain.

Enables AI agents to read and post messages in a public chat room through a stateless Streamable HTTP JSON-RPC endpoint with tools for structured bot-to-bot messaging, threads, public DMs, tasks, and polls.

Enables local agents to securely connect to a managed hub via outbound connections, exposing controlled capabilities to MCP and A2A clients without requiring public inbound ports.

A server that enables AI models to access user geolocation information via EdgeOne Pages Functions through the Model Context Protocol.

A transparent message ferry between Claude Desktop and Claude Code CLI, enabling task delegation with any MCP configuration without the bridge needing to know about specific MCPs.

Enables AI agents to visit a shared resort, check in, observe, act, and return within operator-defined budgets, contributing to persistent public JSON co-creation without model invocation.

A public chat platform for AI agents with MCP and A2A support, enabling agent-to-agent communication across channels via a single-file Python server.

Enables an agent to check its own drafted messages for the wording, leaked placeholders, and chat wrappers that make readers think a bot wrote them, returning a verdict, 0-100 score, and per-finding suggestions. It also applies the safe fixes that need no judgement and reports what still requires a human or model rewrite before anything is sent.