Your MCP Needs Instructions and a Front Door

You have an MCP server. The tools work, authentication works, and someone has successfully used it from their assistant. There is a URL in the documentation. From an engineering perspective, a reasonable amount of the job is done.

From a customer's perspective, there may be no visible difference between your product having an MCP server and not having one.

This is an awkward gap to discover after a launch. We tend to count the people who found the instructions, understood them, connected everything, and persisted until it worked. They're useful customers to learn from. They're also the survivors of a small installation obstacle course.

At Synthflow, our early MCP users were people technical enough to use Claude Code. Adding a Claude connector provided another route in. That matters when interpreting usage: the people willing to configure the first version aren't necessarily everyone who would benefit from it.

Before building your own copilot, I'd make the useful workflows available through the assistants your customers already use. Claude plugins and OpenAI plugins for ChatGPT and Codex provide ways to package connections and skills. The installation and distribution details differ, so check the route for the product you're targeting. The aim is straightforward: make the connection something a customer can discover and use without taking a particular interest in your infrastructure.

Distribution gets them through the door. There's still the question of what happens inside.

Suppose a customer asks an assistant to investigate a failed call. You expose tools for fetching the call status, reading the transcript, and inspecting configuration. This sounds sufficient. All the necessary nouns are present.

But a tool list doesn't necessarily explain how to investigate anything. The assistant could start with the transcript, find a plausible-looking sentence, and build a very confident explanation around it. Meanwhile, the call status might show that the failure happened before a conversation even started.

Someone experienced with the product would check that first. They'd know which failures require which evidence, what to inspect next, and when there isn't enough information to make a claim. That knowledge often lives with support and engineering. Publishing the API doesn't automatically publish the method.

A skill gives you somewhere to describe it. For this illustrative investigation, it could tell the assistant to check status first, inspect the relevant evidence, compare configuration where appropriate, and explain what remains uncertain. The useful part is the sequence and judgement, not another paragraph asking the model to be an exceptionally helpful expert.

It also needs testing. An instruction to check the status isn't evidence that the assistant checked it. Run representative investigations, inspect the tool calls, and see whether the answer follows from what was actually retrieved. If it still leaps to a conclusion, the nicely formatted skill has not solved the problem.

I'd package a few workflows you've already seen customers attempt through MCP. Give them names people recognise, a clear route to installation, and instructions grounded in how those jobs should be done. An investigation skill is easier to evaluate when you know what a good investigation looks like.

Then test the whole journey with someone who wasn't involved in building it. Ask them to connect the product and complete a task. Notice where you feel the urge to reach over and help.

Those are the instructions you haven't shipped yet.