Here is something nobody talks about. You can set up Claude Code with every MCP server imaginable. Supabase for your database. Axiom for your logs. GitHub for your repos. An issue tracker for your tickets. You can build yourself the most connected, most context-rich AI development environment that has ever existed. And it still will not solve the actual problem.
The problem is not access to information. The problem is that information only lives on your machine.
The One-Player Game
A developer with Claude Code and a stack of MCP connections is incredibly productive. They can query the database, search the codebase, read logs, check deployment status, and implement features without ever leaving the terminal. It is a genuinely powerful setup.
But it is a single-player game. Everything that happens in that session — the reasoning, the architectural decisions, the tradeoffs considered, the context gathered — exists in one place: that developer's Claude session. The moment the session ends, it is gone.
Your product manager cannot see what was discussed. Your designer cannot weigh in on the approach. Your other developers cannot learn from the decisions that were made. The accumulated context from an hour of deep technical exploration evaporates like it never happened.
The Huddle Workaround
Some teams try to work around this. They gather everyone around one developer's screen. "Let me show you what Claude found." Someone shares their terminal in a call. The team discusses the architecture together, with Claude as a shared tool.
This almost works. For about twenty minutes, the team is thinking together with AI. The conversation is rich. Decisions get made. Everyone is aligned.
Then the call ends. And where do those decisions live? In a Claude session that belongs to one person. There are no meeting notes unless someone manually writes them. There is no record of the alternatives that were considered and rejected. There is no structured output that another tool can consume.
The developer is now left holding the bag. They have to remember everything that was discussed, translate it into implementation, get it deployed, and then somehow communicate what changed to the rest of the team. The testers need to know what to verify. The PM needs to know what shipped. The other developers need to know what conventions were established.
All of that context transfer happens manually. Through Slack messages, Notion docs, standup updates, and pull request descriptions that nobody reads carefully enough.
The Information Silo You Built on Purpose
This is the irony. MCP was designed to give AI tools access to shared systems. Your database is shared. Your logs are shared. Your repo is shared. But the AI session where all of that information gets synthesized, discussed, and turned into decisions? That is private. That is siloed. That is locked inside one developer's terminal.
You built the most connected AI workflow possible, and the output is still trapped in a single-player experience. The information flows in, but the knowledge never flows out.
Every other developer on the team has to build their own context from scratch. They connect the same MCP servers. They ask Claude the same questions. They rediscover the same patterns. They might even make contradictory decisions because they never saw what was decided in someone else's session.
What the Workflow Should Look Like
The fix is not better MCP connections. The fix is making the AI session itself collaborative. Not after the fact, not through manual translation, but natively.
The team should be able to sit down together — or async, in their own time — and have a conversation with AI that has full context of the codebase, the database, the deployment history, and every decision the team has ever made. When that conversation produces a decision, it should be captured automatically. When it produces an action item, that item should flow directly into the implementation pipeline. When the implementation ships, the team should see exactly what changed and why, without anyone having to write it up.
No context lost between sessions. No knowledge trapped on one person's machine. No manual translation from discussion to spec to implementation to changelog. The conversation is the workflow.
The Missing Layer
This is what we built DevSpec to be. Not another MCP server. Not another AI coding tool. The collaborative layer that sits between your team's thinking and your team's shipping.
The same intelligence you get from Claude Code with all its MCP connections, but in a shared environment where the whole team participates. Where decisions compound across sessions. Where action items flow to developers with full context already attached. Where testers see exactly what changed. Where the PM can check in without scheduling a meeting.
AI-powered development tools have gotten extraordinarily good at the single-developer experience. What has not caught up is the team experience. The workflow where multiple people think together, decide together, and ship together — with AI amplifying every step.
That is the gap. And filling it is not about adding more connections to your terminal. It is about making the AI session itself something the whole team can be part of.