MCP Registry: The Complete Guide to Hosts, Clients, Protocols, and AI Agents

What is the MCP Registry?

The MCP Registry is an open catalog and API for publicly available Model Context Protocol servers. Launched in preview in September 2025 by the MCP maintainers at GitHub, it serves as the canonical source of truth for discovering, publishing, and connecting to MCP servers.

Think of it like npm for AI tools. Instead of manually searching GitHub repos for MCP server configs, copying JSON manifests, and wiring up connections by hand, the registry gives AI applications and tool clients a machine-readable catalog they can query via a standardized OpenAPI interface.

Why a Registry Matters

Before the registry, connecting an AI agent to MCP servers required:

1. Finding the server repo manually
2. Copying transport configuration (URLs, commands, arguments)
3. Configuring authentication credentials
4. Hoping the server was still maintained

The registry solves this by letting MCP clients query a central API, filter by capability or policy, and install servers without copying manifests by hand.

Registry Architecture

The MCP Registry is built on several key architectural principles:

• Open Catalog — An OpenAPI-backed metadata service that anyone can query
• Federated Discovery — Subregistries (public or private) can ingest, augment, or mirror upstream data. An enterprise can run its own private registry while syncing approved servers from the public one
• Extensible Metadata Model — The schema supports additional properties like tags, categorization, and trust scores that downstream registries can layer on
• Namespace Ownership — Publishing uses GitHub-based namespaces (io.github.username/*) authenticated via OAuth, with domain namespaces validated through DNS or HTTP proof (TXT records or challenge endpoints)

What the Registry Stores

The registry is a metadata catalog, not a runtime environment. It stores:

• Server name, description, and version
• Transport protocol support (stdio, Streamable HTTP, SSE)
• Authentication requirements
• Endpoint URLs or command configurations
• Publisher identity and namespace

It does not execute servers, proxy traffic, or enforce runtime security. Trust boundaries are limited to metadata validation and moderation.

Current Limitations

The registry is still in preview. The MCP team has noted:

• No durability guarantees — data may change
• Breaking changes remain possible
• Cryptographic validation of server payloads is a future goal, not yet implemented
• Moderation uses a hybrid human + automated approach via GitHub issues

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MCP Host vs. MCP Client

These two terms are often confused but represent distinct roles in the MCP architecture.

MCP Host

The MCP Host is the application that the user directly interacts with. It is the top-level container that manages one or more MCP clients and controls permissions, security, and user consent.

Examples of MCP hosts:

• Host: Claude Desktop | Description: Anthropic's desktop app that runs MCP servers locally
• Host: Claude Code | Description: CLI-based coding agent with built-in MCP support
• Host: VS Code (Copilot) | Description: IDE with MCP integration via extensions
• Host: Cursor | Description: AI-native code editor with MCP client built in
• Host: Windsurf | Description: AI development environment supporting MCP

Responsibilities of a host:

• Launching and managing MCP client instances
• Enforcing security policies (which servers can connect, what tools are allowed)
• Handling user consent for tool execution
• Managing the lifecycle of server connections

MCP Client

The MCP Client is a protocol-level component that maintains a 1:1 connection with a single MCP server. It lives inside the host and handles the actual protocol communication.

Responsibilities of a client:

• Establishing and maintaining the transport connection (stdio, HTTP, SSE)
• Performing the initialization handshake (capability negotiation)
• Sending JSON-RPC requests and receiving responses
• Discovering available tools, resources, and prompts from the server
• Executing tool calls and returning results to the host

How They Work Together

User <-> MCP Host (Claude Code, Cursor, etc.)
              |
              |-- MCP Client 1 <-> MCP Server (GitHub)
              |-- MCP Client 2 <-> MCP Server (PostgreSQL)
              |-- MCP Client 3 <-> MCP Server (Slack)

The host manages multiple clients. Each client talks to exactly one server. The host decides which servers to connect, what permissions to grant, and whether to allow tool execution.

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MCP Transport Protocols

MCP uses JSON-RPC 2.0 as its message encoding format. All messages must be UTF-8 encoded. The protocol defines two standard transports, plus support for custom implementations.

1. stdio (Standard I/O)

The simplest and most common transport. The client launches the MCP server as a subprocess and communicates through stdin/stdout.

How it works:

• Client spawns the server process
• Messages are sent as JSON-RPC over stdin
• Responses come back on stdout
• Each message is newline-delimited (no embedded newlines allowed)
• stderr is available for logging

Configuration example:

{
  "type": "stdio",
  "command": "npx",
  "args": ["@playwright/mcp@latest"]
}

When to use stdio:

• Local tools (filesystem, browser automation, database)
• Development and testing
• Maximum performance (no network overhead)
• When the server needs access to local resources

Limitation: Only works when client and server run on the same machine.

2. Streamable HTTP

The modern remote transport, replacing the deprecated HTTP+SSE transport from the 2024-11-05 spec. The server exposes a single HTTP endpoint that accepts both POST and GET requests.

How it works:

• Client sends JSON-RPC messages via HTTP POST to the MCP endpoint
• Server responds with either application/json (single response) or text/event-stream (SSE stream for multiple messages)
• Client can open a GET-based SSE stream for server-initiated messages
• Sessions are managed via the Mcp-Session-Id header

Three communication patterns:

• Pattern: Direct Response | Use Case: Simple request/reply tools | Capabilities: Stateless, lowest overhead
• Pattern: Request-Scoped Streams | Use Case: Long operations (video generation) | Capabilities: Progress updates, user prompts, sampling
• Pattern: Server Push Streams | Use Case: Real-time notifications | Capabilities: Tool/resource list changes, server-initiated requests

Configuration example:

{
  "type": "http",
  "url": "https://example.com/mcp"
}

Security requirements:

• Servers must validate the Origin header to prevent DNS rebinding attacks
• Local servers should bind to 127.0.0.1, not 0.0.0.0
• Servers should implement proper authentication

Session management:

• Server assigns a session ID during initialization (Mcp-Session-Id header)
• Client includes the session ID on all subsequent requests
• Sessions can be terminated by the client (HTTP DELETE) or server (returning 404)
• Supports stream resumability via SSE event IDs and Last-Event-ID header

3. SSE (Server-Sent Events) — Deprecated

The original remote transport from the 2024-11-05 specification. Still supported for backwards compatibility but replaced by Streamable HTTP.

Configuration example:

{
  "type": "sse",
  "url": "http://127.0.0.1:7860/gradio_api/mcp/sse"
}

Clients wanting to support older servers should attempt a POST first (Streamable HTTP), and fall back to GET + SSE if the POST returns a 4xx error.

4. Custom Transports

The protocol is transport-agnostic. Implementers can build custom transports over any bidirectional communication channel (WebSocket, gRPC, IPC, etc.) as long as they preserve JSON-RPC message format and MCP lifecycle requirements.

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MCP-Supported AI Agents on Hugging Face

Hugging Face has become one of the most active ecosystems for MCP adoption, with multiple frameworks, an official MCP server, and thousands of MCP-compatible Spaces.

The Official Hugging Face MCP Server

URL: https://hf.co/mcp

The official HF MCP server provides AI assistants with access to the entire Hugging Face Hub:

• Anonymous access: Standard Hub tools and image generation
• Authenticated access: Customizable tool selection, access to thousands of Gradio Spaces, model/dataset search, and ZeroGPU quota management
• Configure your tools at https://huggingface.co/settings/mcp

The server uses Streamable HTTP with Direct Response in production — chosen because it's stateless, has the lowest deployment overhead, and user state (selected tools, Gradio apps) is looked up per-request via token or OAuth.

In its first week, the HF MCP server was accessed by 164 different MCP clients, with roughly 50% using mcp-remote as a bridge.

Tiny Agents (huggingface_hub)

Tiny Agents is a lightweight (~70 lines of Python) MCP-powered agent framework built into huggingface_hub. It demonstrates how minimal an MCP agent can be.

Install:

pip install "huggingface_hub[mcp]>=0.32.0"

Run a pre-built agent:

tiny-agents run celinah/web-browser

Architecture:

• MCPClient class manages connections to MCP servers and discovers tools
• Agent class implements the conversation loop with LLM + tool execution
• Supports all three transports: stdio, HTTP, and SSE
• Works with any model supporting OpenAI-compatible tool calling

Supported inference providers:

Tiny Agents works with 16+ providers including HF Inference, Nebius, Together, Groq, Fireworks, Cerebras, OpenAI, Replicate, and more.

Agent configuration (agent.json):

{
  "model": "Qwen/Qwen2.5-72B-Instruct",
  "provider": "nebius",
  "servers": [
    {
      "type": "stdio",
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    }
  ]
}

Gradio as MCP Servers

Any Gradio application can be exposed as an MCP server, making thousands of Hugging Face Spaces accessible to AI agents:

• Image generation and editing
• Video production
• Document search and analysis
• Virtual try-on and shopping assistants
• 3D model generation
• Audio/video transcription

Gradio Spaces expose MCP endpoints at paths like /gradio_api/mcp/sse, allowing any MCP client to connect directly.

MCP Spaces Ecosystem

Hugging Face hosts 5,700+ MCP-related Spaces, including:

• Space: Consilium MCP Server | Description: Multi-AI expert consensus platform
• Space: SAM 3D Body MCP | Description: Image to 3D human mesh generation
• Space: Website Generator | Description: Screenshot to functional HTML
• Space: TinyCodeAgent | Description: Python code execution agent
• Space: BioMedNorm MCP Server | Description: Biomedical entity extraction
• Space: Gazebo MCP | Description: Robotics simulation
• Space: MCP Web Search | Description: Real-time web search

Agents-MCP Hackathon

Hugging Face hosted a dedicated Agents-MCP Hackathon that produced multiple production-quality MCP servers and agents, demonstrating the breadth of what's possible when agents can discover and use tools through a standardized protocol.

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The Broader MCP Infrastructure

Key Players

• Organization: GitHub/Microsoft | Role: MCP protocol maintainers; led by Toby Padilla
• Organization: Anthropic | Role: Original MCP creators; Claude is the reference MCP host
• Organization: Hugging Face | Role: Major ecosystem contributor with official MCP server + Gradio integration
• Organization: Docker | Role: MCP server packaging and registry efforts
• Organization: Linux Foundation | Role: Accepted Agentgateway into its portfolio
• Organization: Solo.io | Role: Contributed Agentgateway for agent-to-agent/tool/LLM governance

Agentgateway

The Agentgateway project (contributed by Solo.io, hosted by Linux Foundation) is a data plane that governs agent-to-agent, agent-to-tool, and agent-to-LLM interactions. It supports emerging protocols including both MCP and A2A (Agent-to-Agent), providing a governance layer for enterprise agentic workflows.

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Getting Started

1. Browse MCP servers on the AICoach MCP Registry or the official MCP Registry
2. Connect to the HF MCP server by adding https://hf.co/mcp to your MCP client
3. Try Tiny Agents with pip install "huggingface_hub[mcp]" and tiny-agents run celinah/web-browser
4. Build your own MCP server using Gradio and deploy it to Hugging Face Spaces
5. Publish to the registry to make your server discoverable by the entire ecosystem