Topic
MCP for teams
Using the Model Context Protocol to give a whole team's AI tools one shared context layer, local or remote.
11 posts
RAG vs MCP: when to retrieve, when to share context
RAG retrieves chunks from documents; MCP connects tools to live context and actions. Here is the real difference, when to use each, and how they work together.
How to use MCP across all your AI tools
A practical guide to connecting Claude Code, Cursor, ChatGPT, and more over MCP so every AI tool reads the same context, with the steps that actually matter.
Local vs remote MCP servers: which your team needs
Local MCP servers run on your machine, remote ones are hosted at a URL. Here is how they differ, which tools each suits, and why teams usually want both.
Is MCP secure? What teams should know
MCP is a protocol, so its security depends on how servers are built and run. Here are the real risks, the controls that matter, and what to check before you connect.
Best MCP servers for engineering teams (2026)
The best MCP servers for engineering teams in 2026: GitHub, issue trackers, databases, observability, and a shared context server, with what each is good for.
MCP vs API: what is actually different
MCP and APIs both connect software, but they solve different problems. Here is the real difference, when each one fits, and why MCP sits on top of APIs.
What is MCP (Model Context Protocol)? A 2026 guide
MCP is an open standard that lets AI tools read outside context and call tools through one protocol. Here is what it is, how it works, and why it matters.
How to set up a shared MCP context server for your team
Set up a shared MCP context server your whole team's AI tools read: curate the context, choose local or remote, connect tools, scope access, and let it update.
What is a remote MCP server (and when teams need one)?
A remote MCP server is a hosted endpoint any AI tool can connect to over the network. Here is how it differs from a local server and when a team should use it.
How to give Claude Code your whole project context
Give Claude Code your whole project context, not just one repo's CLAUDE.md. Here is how a shared source keeps it current across repos, tools, and teammates.
MCP for teams: one context layer across your AI tools
MCP for teams turns scattered docs and decisions into one context layer every AI tool reads, so Claude Code, Cursor, and ChatGPT share the same source.
Get your team's AI tools on the same page
BaseThread is the shared context-graph that Claude Code, Cursor, and every AI tool your team uses can read, so no one re-explains the same context twice.
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