MCP Studio Blog
Tutorials and workflows for teams shipping context-powered AI with MCP Studio. Start with a 2-minute setup, then scale quality and impact with analytics.
Showing 42 of 42 posts
Services, boundaries, and the reasons they exist already live in your stack. This is how a software architect keeps that context available to every LLM in the flow, at the stage of the change that needs it.
Search can find the public web. Your decisions, your repository, and this week's customer questions are not on it. Context engineering is how a technical writer places that stack where an implementation will ask.
Give ChatGPT your docs, repository, or brand kit as live context. Build the server in MCP Studio with no code, turn on developer mode, and add the URL as an app.
Give Codex your docs, repository, or brand kit as live context. Build the server in MCP Studio with no code, add its URL to Codex, and check it with /mcp.
Add the current spec and runbooks to MCP Studio, pick search and reference tools, and check that the answer quotes the exact timeout and error code.
Index your developer portal and sample repository, turn on the reference and code tools, and confirm a header question cites the reference, not the quickstart.
Upload or connect your internal handbook, keep the server private, issue one token per person, and ask a question only an employee should be able to answer.
Name the job, add one source, choose the tools, and deploy. MCP Studio hosts the server, so you get a URL your AI client can call without writing any code.
Deploy your API reference as an MCP server with API lookup turned on, connect your agent, and check one endpoint's method, path, and required field against the page.
Add a second source only when it answers a question the first cannot. Deploy one URL in MCP Studio, then test each source with a question only it can answer.
Most MCP bugs are a client that never called the server. Prove the connection with curl first, then check the client, the index, and the content in that order.
A documentation gap is a question people keep asking that nothing in your content answers. Here is how to find those questions in MCP Studio analytics and close them.
A wrong answer from an MCP server has one of three causes, and each has one fix. Copy the question, open the citation, change one thing, and ask the same question again.
An MCP server that won't connect usually has the wrong URL saved or a client that was not restarted. Prove the server with curl, then fix the client.
A connector usually exists on the account but is switched off in the current chat. Turn it on in Connectors, and confirm the saved URL contains /api/mcp/.
Cursor reads MCP config at startup. If a server is missing, the JSON has an error, a private server has no token header, or Cursor was not restarted.
Add your help center to MCP Studio, connect it to the assistant your support team uses, and check that a real customer question comes back with the article you would have sent.
Paste a GitHub URL into MCP Studio, turn on code example lookup, connect your agent, and ask for a function you can open in the tree. A private repo keeps the server private.
Turn your help center into a hosted MCP server, add it in Claude as a custom connector, and confirm the reply cites the page your team would have opened.
Deploy the runbook as a private MCP server, add its access token to Cursor's MCP settings, restart, and ask a question only the runbook can answer.
An AI agent searches your docs through a tool, not your site's search box. Deploy the docs in MCP Studio with search turned on, connect the agent, and check that it cites the right page.
Write down three questions your team already asks, put only the documents that answer them on an MCP Studio server, and check that each reply cites the right page.
People ask how to rotate a key while the page says credential lifecycle. Rename the heading, put the answer first, and ask the question again to prove the edit.
People ask about a leaked secret; your docs say rotate signing keys. Index your docs in MCP Studio and check the same page comes back however the question is worded.
Request volume shows the server was used. Retrieval quality shows the passage it returned held the fact. Write a fixed list, mark each passage yes or no, and rerun it after every edit.
Site traffic shows who opened a browser. Your MCP server shows which page the agent was handed. Here is how to read that list and act on what does not match.
An agent answers from your words when the page that states the fact is connected. Build that server in MCP Studio, require a citation, and test one question the docs do not cover.
Write down a few real questions, the page each one should cite, and one question the docs do not answer. A pass is the right link and the right fact, not a fluent paragraph.
Upload a handbook that never had a URL. MCP Studio reads the text, hosts the server, and your AI client cites the exact page that holds the rule.
Pasting a page helps one chat. An MCP server gives every chat the right passage from your docs, linked to its page. Build one in MCP Studio and check the citation.
Give Claude a live link to your handbook, brand kit, or docs. Build the server in MCP Studio with no code, then paste the URL into Claude as a custom connector.
How to give an AI assistant the files your work actually lives in, why an image needs to come back as an image, and how we tested it by inventing a company.
Why we spent a full day taking the way MCP Studio picks the passages your AI assistant reads to the next level, what changed, and the one behavior we are proudest of.
A setup guide for combining a GitBook MCP endpoint with a code repository behind a single endpoint, what a connected MCP source contributes, and what a combined server should say when neither source covers the question.
How to choose between a public and a private MCP server, what a scoped access token does, what happens to your content when you disconnect a source, and how to decide all of it against your own policy.
The first step is a diagram of the app you want. Each box becomes a source. Those sources become one MCP server. Then your editor writes the first pass with the whole stack in context.
A practical guide to building a docs MCP around one migration task, testing it with questions whose answers you already know, and improving it from real request evidence.
Why a confident draft can name a function that is not there, the smallest context that prevents it, and the habit of asking for the source behind an unfamiliar method.
A review you can run yourself: which signals are worth watching, how to read them together, why a question that found nothing is the most useful thing in the data, and which views are free and which are paid.
What a Docs MCP server does for the question you just asked, how to write documentation that retrieval can land in, and the behavior that matters most: returning nothing rather than something nearby.
Five things worth scoring separately, one question your documentation cannot answer, and a complete worked comparison you can repeat on any MCP server.
Pick one source, write three questions, run the wizard, connect your AI client, and use those questions to check that the answers are real.