Agent MCP Deployment Platform - AI Agent A2Z Middleware

AI Agent and MCP Server Hosting and Deployment Service. Agent and MCP Deployment Middleware and Hosting Infrastructure - DeepNLP x AI Agent A2Z

Website | GitHub | AI Agent Marketplace | AI Agent A2Z

Introduction

DeepNLP x AI Agent A2Z (aiagenta2z.com) provide public hosting service of AI Agent and MCP Deployment. Users can get a unique live subdomain endpoint for their agent/mcp project, which can be distributed and used in ChatGPT Apps Store, Cursor, etc to connect and use , e.g. Live URL: ${owner_name}.aiagenta2z.com/${repo_name}/mcp

Visit Deployment Panel

Features

1. Various Deployment methods: template, github_repo, and source code

2. GitHub/Source Code: Support both Python/Typescript, which is just like how you start your Agent locally, you can deploy in our cloud container and save money of without the heavy cost of renting a cloud server or get a domain name by yourself.

3. Templates: We provides 20+ templates in various business models, such as selling product and digital resources e-commerce products agent/mcps as resources, vendors and content creators can expose their physical goods, digital resources (documents,files,online courses) etc to ChatGPT/Cursor.

4. SubDomain URL: Each user can have a unique subdomain URL for your agents, able to verification and hosting services.

5. Domain Verification: We support subdomain verification for various platforms, such as OpenAI, WeCom, WeChat, DingTalk, etc.

6. API Monitor and Credit Rewards: You can visit the Deployed Agent API Dashboard to see the metric of your Deploy Agent & MCP and Billing Credits earned.

Examples Deployed

Deployment Type	Framework / Package	Example Folder	Live URL	Description
GitHub Repo	Quickstart (Typescript)	GitHub quickstart/mcp_typescript	https://quickstart.aiagenta2z.com/mcp_typescript/mcp	Typescript MCP Quickstart Example
GitHub Repo	Quickstart (Python)	GitHub quickstart/mcp_python	https://quickstart.aiagenta2z.com/mcp_python/mcp	Python MCP Quickstart Example
GitHub Repo	LangChain DeepAgents (Multi-Agents)	GitHub langchain_deepagents/deep_research	https://langchain-ai.aiagenta2z.com/deep_research/chat	LangChain Examples DeepResearch Agent of Tavily and Gemini model
GitHub Repo	LangChain DeepAgents (Agents + Skills)	GitHub langchain_deepagents/content-builder-agent	https://langchain-ai.aiagenta2z.com/content-builder-agent/chat	LangChain Examples AI Content Builder + Image Generation Agent with Skills + Subagents
GitHub Repo	ChatGPT Apps SDK	GitHub ChatGPT Apps	https://derekzz.aiagenta2z.com/solar-system_server_python/mcp	ChatGPT App with MCP + Assets
GitHub Repo	AgentScope + Qwen3 + Tavily	GitHub agentscope_examples/deep_research_agent	https://agentscope.aiagenta2z.com/deep_research_agent/chat	AgentScope Deep Research Live Chat
Source Code	Python - Perplexity API DeepResearch	GitHub https://github.com/jjjmc/perplexity_agent	https://jjjmc.aiagenta2z.com/perplexity_agent/mcp	Perplexity API Deep Search Agent
Source Code	Typescript - Fortune Telling Agent (ChatGPT App)	GitHub chatgpt-apps/fortune-compass-agent	https://derekzz.aiagenta2z.com/fortune-compass-agent/mcp	Fortune Telling App MCP
Template Product	Deployment Templates		-	Deployment Template of OpenClaw/E-Commerce/etc

Supported SDK and Packages

We provide python and typescript runtime suitable for various packages and framework, such as langchain , agentscope, etc.

Quickstart

1. Typescript - MCP Server

Overview

This typescript MCP server implements one tool greeting and one resource index (html in assets folder) in a mcp.streamable_http_app() and expose “/mcp” endpoint

Deployment Steps

1. Create new AI Service at AI Service Project

The quickstart tutorial is using (quickstart/mcp_typescript) which follows (username/project_name) format, you can change to (${your_user_name}/mcp_typescript)

2. Go to A2Z Deployment Panel

Choose Method Github

Public URL: https://github.com/aiagenta2z/agent-mcp-deployment-templates/tree/main/quickstart/mcp_typescript

Entry Point Starting Script:

pnpm --prefix ./quickstart/mcp_typescript install
pnpm --prefix ./quickstart/mcp_typescript build
pnpm --prefix ./quickstart/mcp_typescript start

3. Click Deploy

Results

You can test the live /mcp URL on your client, such as Cursor, ChatGPT, Claude Code, etc.

{
    "mcpServers": {
        "quickstart-mcp-node": {
          "url": "https://quickstart.aiagenta2z.com/mcp_typescript/mcp"
        }    
    }
}

Test resource in /assets folder

https://quickstart.aiagenta2z.com/mcp_typescript/assets/index.html

See examples README.md for more details.

2. Python - MCP Server

Overview

This server implement two tools add greeting in a mcp.streamable_http_app() at “/mcp” endpoint

Deployment Steps

1. Create new AI Service at AI Service Project

The quickstart tutorial is using (quickstart/mcp_python) which follows (username/project_name) format, you can change to (${your_user_name}/mcp_python)

2.Go to A2Z Deployment Panel

Choose Method Github

Public URL: https://github.com/aiagenta2z/agent-mcp-deployment-templates/tree/main/quickstart/mcp_python

Entry Point Starting Script:

uvicorn quickstart.mcp_python.server:app

3.Click Deploy

Results You can test the live /mcp URL on your client, such as Cursor, ChatGPT, Claude Code, etc.

{
    "mcpServers": {
        "quickstart-mcp-python": {
          "url": "https://quickstart.aiagenta2z.com/mcp_python/mcp"
        }    
    }
}

See examples README.md for more details.

3. Typescript - Agent

4. Python - Agent

Tutorial

1 Deploy From Source Code

1.1 Python - Case Hosting a Perplexity Deep Research Agent

Lets’ say you want to implement a Perplexity Search API Customized Search API based MCP server and want to expose a tool perplexity_research_agent(messages: List[Dict[str, str]]) perplexity_search_agent(question: str) for users to use.

The source code can be found in GitHub Perpleixty Agent

And you have already prepared below information:

Requirements

## Create a new project and Register AI Service
unique_id: jjjmc/perplexity_agent

## archived source code
git clone https://github.com/jjjmc/perplexity_agent
cd perplexity_agent

## archive the file
tar czvf perplexity_agent.tar.gz ./*
## zip on windows
perplexity_agent.zip

## Drag and Upload to Source

## Starting Command
uvicorn server:app

## pip requirement files in the archived source code perplexity_agent_code.zip
requirements.txt

Step 1. Go to Workspace->Agent Deployment and visit the Deployment Workspace

Select the project to deploy ‘jjjmc/perplexity_agent’

Step 2. Switch Tab: Custom Python/JS

Drag and drop the source code archive file perplexity_agent_code.zip to upload

Step 3. Choose Config and Deploy

Deploy Region: global for avoid most ip restriction)
Entry Point: Input uvicorn server:app, this is the command that you use to start MCP/Agent server locally, for example we have a server.py file and an app class, and we use uvicorn to start the mcp, please avoid specifying any ports and we use assign dynamically.
Environment Variables: We put PERPLEXITY_API_KEY and DEEPNLP_ONEKEY_ROUTER_ACCESS as key-value pairs in this field. Note that your access key is safe and we will use pass the keys as variables in the requests to start your service in the container. It’s equivalent to .env files in your uploaded sources.

PERPLEXITY_API_KEY=xxxxx
DEEPNLP_ONEKEY_ROUTER_ACCESS=xxxxx

Step 4. Deploy Click deploy button and please wait a while for the deployment to complete and you will find your subdomain live url ready!

MCP SERVER URL: https://jjjmc.aiagenta2z.com/perplexity_agent/mcp

1.2 Typescript - Case Hosting a Fortune Telling Divination Apps with UI

The source code can be downloaded from Github

Create AI Service at AI Service project

Deployment from Source

Archive your source code to .zip or .tar.gz file

## archived source code
git clone https://github.com/aiagenta2z/agent-mcp-deployment-templates
cd chatgpt-apps/fortune-compass-agent

## archive the file
tar czvf fortune-compass-agent.tar.gz ./*
## or zip
fortune-compass-agent.zip

Step 1. Go to Workspace->Agent Deployment and visit the Deployment Workspace

Select the project to deploy ‘your_user_name/fortune-compass-agent’

Step 2. Switch Tab: Custom Python/JS

Drag and drop the source code archive file fortune-compass-agent.tar.gz to upload

Step 3. Choose Config and Deploy

Deploy Region: global for avoid most ip restriction)
Entry Point: this is the command that you use to start MCP/Agent server.

For typescript deployment, we usually use pnpm,npx, etc command to start the server. Since we are building from source code, the platform will first install dependency, build to ./dist/server.js and start from the js code there.

pnpm install
pnpm build
pnpm start

Environment Variables: If applicable

Step 4. Deploy Click deploy button and wait for completion.

SERVER URL: https://derekzz.aiagenta2z.com/fortune-compass-agent/mcp

{
   "mcpServers": {
       "fortune-compass-agent-live": {
         "url": "derekzz.aiagenta2z.com/fortune-compass-agent/mcp"
       }
   }
}

See README for more details

2. Deploy From GitHub Repo

Deployment from Github Repo is as easy as you git clone project from web start the mcp/agent server locally. Imagine you are tenants on a shared hosting cloud service, and you just need to prepare a multi-line entry_point.sh shell script to let us know how you want to run the server.

mode	url
public	example: https://github.com/openai/openai-apps-sdk-examples
private	Connect GitHub & Load Private Repos, Allow app ‘ai agent a2z’ to connect and clone your code

2.1 ChatGPT App Example

Step 1. Switch Tab to GitHub

Choose Public URL or Private Repository to allow us to connect to your private GitHub repos.

GitHub Repository URL: https://github.com/openai/openai-apps-sdk-examples
Deploy Region: Global or region for your plans applicable

Step 2. Prepare Your Entry Command Entry Point (Startup Scripts): This part is important and you might need to try various command. Hints:

1. Enter one command per line. For multi-process Agent/MCP Deployment (like ChatGPT Apps Solar System), Include both the build and run commands.

2. The last line should be the main MCP/Agent server running command starting with uvicorn,python,pnpm,npm etc. Don’t include any –port flags, the ports will be assigned automatically.

3. Dependency Installation: Don’t add node or pnpm install, cloud platform will handle it by reading your package.json, file.

4. Static File Build and Serve: ChatGPT App will build static resources (Html/Css/Js) from the ‘src’ folder, and we have cached the prebuild example also. and you can also put the build command yourself.

5. Static File Serving: The example in the chatgpt app example started two web services: ‘pnpm run serve’ serve prebuild static file on port 4444, and uvicorn solar-system_server_python.main:app is the main MCP server starting endpoint. Successfully deployed logs include Using BASE_URL http://localhost:4444 for generated HTML and Uvicorn running on http://0.0.0.0:8000. You have to make sure your command ‘pnpm run serve &’ add trailing ‘&’ to run in the backend and prevent blocking the scripts.

6. Any Unknown Failures: Please remember to contact us via raising issues on GitHub and we will help you resolve the issues immediately.

pnpm run build
pnpm run serve &
uvicorn solar-system_server_python.main:app

or another app “kitchen-sink-mcp-node”

pnpm run build
pnpm run serve &
pnpm --filter kitchen-sink-mcp-node start

Environment Variables: If Applicable

Step 3. Deploy Click deploy button and please wait a while for the deployment process to complete and you will find your subdomain live url ready!

And wait for server to finish and once it’s done, you can copy and paste the server url in MCP client such as ‘cursor’,

MCP SERVER URL: `https://derekzz.aiagenta2z.com/solar-system_server_python/mcp

And you can see if it’s deployed successfully!

Copy and Paste into your Client

{
  "chatgpt-solar-system-mcp": {
    "url": "https://derekzz.aiagenta2z.com/solar-system_server_python/mcp"
  }
}

See ChatGPT App examples README.md for more details.

2.2 AgentScope Example-DeepResearch Agent

Example GitHub

We will implement and deploy a agentscope based deep-research agent, converting the original deep research example to an FastAPI Server and expose the “/chat” API endpoint so that users can call the deep research agent by parameters messages and get the deep research reports

Item	Description
unique id	agentscope/deep_research_agent
Live Chat URL(POST Only)	https://agentscope.aiagenta2z.com/deep_research_agent/chat
Playground	https://agent.deepnlp.org/?agent=agentscope/deep_research_agent

You can also use the Agent Router playground (WebUI) to start chatting with your live agents. Visit the URL https://agent.deepnlp.org/?agent=${unique_id}. Note that the returned streaming chunks should follow specific formats to work with the Agent Router Web UI. See Doc #assembly_message function for more details

See agentscope examples README.md for more details.

2.3 LangChain DeepAgents

We wrap two examples content-builder-agent and deep_research of LangChain DeepAgents multi agents examples and bring it live. Example GitHub

This guide shows how to convert a LangChain DeepAgents content-builder-agent into a production-ready live service using BaseLiveRuntime from ai-agent-marketplace

The runtime wraps your agent and exposes a FastAPI streaming /chat endpoint automatically.

QuickStart

from ai_agent_marketplace.runtime.base import *

async def content_builder_stream_generator(
    agent: Any,
    user_query: str,
    **kwargs
) -> AsyncGenerator[str, None]:
    """
    """
    ## more

runtime = BaseLiveRuntime(
    agent=agent,
    stream_handler=content_builder_stream_generator
)

## Returned a FastAPI based app with /chat endpoint
app = runtime.app

And the detailed deployment steps can be found in LangChain Deployment examples, please follow README.md for more details.

3 Deploy From Template (Beta)

Use Case 1 Selling products

Domain Verification

The AI Agent A2Z Agent & MCP platform provides subdomain verification services to help you submit your deploy agents to various Apps Store and verify that you own the domains, such as ChatGPT App Store, MCP Official Registry, WeCom (Tencent), WeChat, DingTalk, etc.

Go to Deployment -> Deployment Configuration -> Domain Verification tab

You can choose your sites that need to verify for your customized subdomain: https://{username}.aiagenta2z.com Switch the tab, fill in the filename and content value, the just one click “Verify Domain”.

App	Verification URL
ChatGPT App Store	https://{username}.aiagenta2z.com/.well-known/openai-apps-challenge
MCP Official Registry	https://{username}.aiagenta2z.com/.well-known/mcp-register-challenge
WeCom (Tencent)	https://{username}.aiagenta2z.com/WW_verify_xxxxxx.txt
WeChat	https://{username}.aiagenta2z.com/MP_verify_xxxxxx.txt
DingTalk	https://{username}.aiagenta2z.com/verify_xxxxxx.txt

1. ChatGPT App Submission and Domain Verification

On the ChatGPT App Manage Page (https://platform.openai.com/apps-manage), you can submit your AI Agent by filling the form. You need to prepare a MCP Server URL (e.g. https://derekzz.aiagenta2z.com/fortune-compass-agent/mcp). Copy and paste the verification code under the file path

https://derekzz.aiagenta2z.com/.well-known/openai-apps-challenge

You can go to the Domain Verification tab of AI Agent A2Z Deployment (https://deepnlp.org/workspace/deploy) platform.

Fill the form of openai-apps-challenge with the code on the platform. Then click Verify Domain to add a record. Please wait a while for the record to work. If you want to change the content, just add a new record and the content will be overridden.

Agent API Dashboard

Agent API Dashboard helps you monitor statistics and credits account

After you have deployed your AI Agent (e.g. https://derekzz.aiagenta2z.com/fortune-compass-agent/mcp). The Deployed Agent API Dashboard will monitor the incoming traffic to the endpoint.

You can also visit the detail page to set API credit per call and you can start earn credits from your hard work. Visit the Billing Credits for detail reports.

Agent Router

Launch Your AI Agent with real user traffic by visiting Agent Router Website,

You can launch and expose your AI Agent to real users and attract user visit traffic via the Agent Router Web (https://agent.deepnlp.org) by registering either a /chat Agent endpoint or a /mcp MCP endpoint.

Please visit the Agent Router Docs for more details.

1. Agent Router with Chat Endpoint

For agent API with /chat endpoint and messages input, you can configure your AI Service “API Endpoint” with a POST interface that accepts the messages format and returns properly structured streaming chunks (including required fields such as type, format, content, and message_id) so the Router can correctly render text, markdown, HTML, images, or other MIME types in real time;

Once configured, access your agent publicly at

https://agent.deepnlp.org/?agent=${unique_id}

${unique_id} follows {user_id}/{project_name} format

https://agent.deepnlp.org/?agent=agentscope/deep_research_agent

Enabling live routing, streaming UI rendering, and ecosystem exposure.

2. Agent Router with MCP Endpoint

To quickly get started with the /mcp endpoint, first click the Green Add button. In the panel, switch to the New Agent tab. Enter your MCP name and MCP server URL just as you would in other MCP clients, then click Connect and wait for a successful connection.

Once connected, click Add Agent to complete the setup. Your agent will then be available as a web-based MCP client, allowing you to explore and use its tools and capabilities.

For MCP API with /mcp endpoint, use the green button of “Add Cross” in the Router UI to connect your MCP server URL and automatically explore its tools like any MCP client; Once configured, access your agent publicly at https://agent.deepnlp.org/?agent={unique_id}, enabling live routing, streaming UI rendering, and ecosystem exposure.

3. Agent MCP Usage Rankings

Agent and MCP APIs Usage Ranking https://deepnlp.org/agent/rankings

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