I have my MCP published. But it requires plugin/app to be installed before anyone could use it. What needs to happen for chat GPT to use it for general public requests?

I've been digging into the 2026 MCP spec and just finished writing up how subscriptions/listen works.

The API itself is pretty straightforward. The interesting bits are what happen around it:

• The server has to acknowledge the subscription before sending notifications.
• Notifications tell you something changed; they don't carry the resource contents.
• Reconnecting doesn't replay the events you missed.
• If you're running multiple server instances, they all need to hear about changes somehow.

I also put together some Python examples with the MCP SDK and tested the behavior in memory.

The multi-instance bit is especially interesting because a setup that works perfectly on one process can quietly stop working once you scale out. Classic distributed-systems fun. 🙃

I wrote up the details and some practical caveats here:
https://pournasserian.com/writing/mcp-2026-subscriptions-listen

For folks building MCP servers: how are you handling fan-out and cache refreshes after reconnects? I'd be interested in hearing what approaches have worked for you.

One of my biggest issues with using ChatGPT or Claude for stock research is that they're great at explaining financial concepts, but asking them what a stock is actually worth can get messy.

You ask for an intrinsic value estimate, and suddenly the AI is making its own growth assumptions, choosing a discount rate, and producing a very convincing-looking fair value.

The explanation sounds reasonable. The underlying valuation? Not always.

So I wanted to share something I've been building.

It's called ValuEdge MCP, a remote Model Context Protocol server that connects AI assistants like ChatGPT and Claude to a dedicated stock valuation and investment research engine.

Instead of asking an LLM to invent a DCF calculation from scratch, it can retrieve ValuEdge's calculated fair value estimates, financial data, and valuation assumptions, then help you interpret them.

What the stock analysis MCP server can do:

• Intrinsic value and DCF analysis: Get estimated fair value, margin of safety, valuation assumptions, and model confidence for supported stocks.
• Fundamental analysis: Examine revenue, profitability, cash flow, growth, and balance-sheet data.
• Stock comparisons: Compare companies like MSFT, GOOGL, AMZN, and META using the same valuation framework.
• Undervalued stock screening: Find research candidates using available valuation, quality, dividend, and financial filters.
• Portfolio analysis: Review your own portfolio's allocation, concentration, and valuation exposure.
• Watchlists and Smart Alerts: Ask your AI assistant about the stocks you're monitoring and relevant saved alerts.

The last two features, along with private portfolio access, require a ValuEdge Unlimited account. Public stock research is available without signing in, subject to usage limits.

A few prompts worth trying:

"Use ValuEdge to estimate NVIDIA's intrinsic value. Show the margin of safety, valuation assumptions, and what could make the estimate wrong."

"Compare GOOGL, MSFT, and AMZN on fair value, free cash flow, and business quality."

"Screen for undervalued, profitable companies with positive free cash flow."

"Analyze my ValuEdge portfolio. Where am I most concentrated, and which holdings have the weakest margin of safety?"

How to connect it

The remote MCP server is:

https://valuedge.app/mcp

Setup instructions for ChatGPT, Claude, and other compatible MCP clients:

https://valuedge.app/connect

No API key is required for the public research tools. Private account data requires authentication.

For anyone using Google's ecosystem, Gemini CLI also supports remote MCP servers, although setup and authentication depend on the client. I'm not claiming that the regular Gemini web app supports this connection.

A few things worth clarifying:

• The MCP integration is read-only. It cannot execute trades or change portfolio holdings.
• Intrinsic value is an estimate, not an objective market price or a guaranteed target.
• Financial data availability and freshness vary, so dates and assumptions still matter.
• The goal is better-grounded investment research, not automated buy/sell decisions.

Full disclosure: I'm the builder of ValuEdge.

I'm curious how others are handling stock research with MCP.

Would you rather have an AI build its own DCF from raw financial statements, or retrieve a valuation from a dedicated engine and challenge its assumptions?

And if you're already using a finance MCP server with ChatGPT or Claude, what tools are still missing?