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Are there yet any clients like you that can consume my new “Streamable HTTP” server I just published to a serverless function host and help me see the effects of having put it out on the internet

Yes, there are already clients and tools available that can connect to your new “Streamable HTTP” server, even if it’s hosted on a serverless function platform.

• The Model Context Protocol (MCP) ecosystem has recently adopted “Streamable HTTP” as a standard transport, allowing clients to interact with servers using plain HTTP requests with optional Server-Sent Events (SSE) for streaming responses¹²³.
• The MCP Inspector tool is specifically designed to test and validate Streamable HTTP servers. It can connect to your endpoint and simulate client behavior, helping you verify that your deployment works as intended⁴.
• MCP clients and SDKs, especially those in TypeScript, already support Streamable HTTP out of the box, and work is ongoing to bring this support to other languages⁴.
• There are also community examples and discussions confirming that remote MCP servers (such as mcp.paypal.com) are accessible using Streamable HTTP clients⁵.

In summary, you can use existing MCP clients and the MCP Inspector tool to interact with and test your Streamable HTTP server right now⁴.

Oh,sorry, I meant, like, an actual chatbot. I already tried MCP Inspector. My server behaves as expected, according to it. But I’m chomping at the bit to ask a chatbot to use it. I can’t find any yet.

There are not yet widely available, ready-to-use chatbots that let you point them at an arbitrary “Streamable HTTP” server and chat through it directly. Most chatbots today-like those built with LangChain, Ray Serve, or Sendbird-support streaming outputs but are typically hardwired to LLM providers like OpenAI or Anthropic, rather than being generic clients for your custom server⁶⁷⁸.

However, there is active development in the community. For example, a TypeScript Slack MCP client is being built that supports Streamable HTTP and aims to provide a chatbot-like interface, but as of last month, it only works with OpenAI and is not yet a general-purpose chatbot for arbitrary Streamable HTTP endpoints⁹. Others in the community are also experimenting with assistants for Discord and similar platforms, but these are not yet publicly available or turnkey for custom endpoints⁹.

In short: you may need to build or adapt an existing open-source chatbot to target your server, as the ecosystem is just starting to catch up with Streamable HTTP as a standard for chatbots.

Oh, sorry, I meant one that will mostly use a major LLM, but that will supplement its research with my new little MCP server.

Thank you for clarifying! You’re looking for a chatbot that primarily uses a major LLM (like OpenAI, Anthropic, or similar) but can supplement its responses by querying your custom MCP/Streamable HTTP server as a secondary source.

Current State of the Art

1. No Turnkey Solution Yet

As of now (mid-2025), there isn’t a widely available, plug-and-play chatbot that natively combines a major LLM with arbitrary MCP/Streamable HTTP research plugins. The ecosystem is moving in this direction, but most chatbots are still tightly coupled to their default LLM providers and do not yet offer easy “bring your own tool” plugin support for custom HTTP endpoints.

2. Closest Existing Approaches

a. OpenAI GPTs \& Plugins

• GPTs (custom GPTs at chat.openai.com) can browse the web and call APIs, but you must define the API schema and sometimes work around limitations (e.g., authentication, streaming support).
• OpenAI Plugins (for ChatGPT) allow calling external APIs, but the plugin system is not fully open for arbitrary endpoints and streaming support is limited.
• Toolformer/Function Calling: Some frameworks let you define “functions” that the LLM can call, but these are usually for synchronous, non-streaming APIs.

b. LangChain \& Similar Frameworks

• LangChain lets you build agents that can call external tools and APIs as part of their reasoning process.
• You can define a custom “tool” that queries your MCP server, and the LLM agent will use it as needed.
• This requires some Python/TypeScript coding, but is the most flexible approach right now.

c. MCP Community Projects

• As you’ve noticed, MCP Inspector is for testing, not chatting.
• Community chatbots (Slack, Discord) are in development, but not yet general-purpose or easily configurable for your endpoint.

What You Can Do Now

Option 1: Build a LangChain Agent

• Define your MCP server as a tool in LangChain (Python or JS).
• The agent will use a major LLM, but when it needs extra info, it can call your server.
• LangChain documentation on tools is a good starting point.

Option 2: Custom GPT with API Actions

• If your MCP server has a simple, RESTful API, you can try defining it as an “action” in a custom GPT at chat.openai.com/gpts.
• Streaming responses may not be fully supported, but you can get basic integration.

Option 3: Wait for MCP Chatbot Projects

• Keep an eye on MCP community projects-there’s clear demand for this, and it’s likely someone will release a more general chatbot soon.

Example: LangChain Tool Definition (Python)

from langchain.tools import Tool

def query_mcp_server(query: str) -> str:
    # Replace with your actual HTTP call to the MCP server
    import requests
    response = requests.post("https://your-mcp-server.com/ask", json={"query": query})
    return response.text

my_mcp_tool = Tool(
    name="My MCP Server",
    func=query_mcp_server,
    description="Useful for getting up-to-date research from my custom MCP server."
)

Then, add my_mcp_tool to your agent’s tool list.

Summary Table

Bottom line: You’ll likely need to use LangChain or a similar agent framework to get this hybrid LLM + MCP research experience today, but the ecosystem is rapidly evolving and more user-friendly solutions are on the horizon!

If you want a code example or more detailed guidance for your specific stack, let me know!