The Model Context Protocol has made it easier for AI assistants and agents to connect to external tools.
An MCP server can expose actions such as:
- Search files
- Read a document
- Create a folder
- Update a task
- Query a database
- Send information to another service
This solves an important interoperability problem. It does not automatically solve the team collaboration problem.
MCP defines how an agent reaches a capability. A shared workspace defines where the team’s work and decisions persist.
What MCP does
MCP provides a standardized way for an AI client to discover and invoke tools offered by a server.
Instead of creating a custom integration for every model and application, a tool provider can expose a set of capabilities through one protocol.
For a file platform, those capabilities might include:
- Listing projects
- Searching content
- Reading files
- Uploading artifacts
- Sharing a project
MCP is valuable because it reduces integration fragmentation.
What MCP does not decide
The protocol alone does not determine:
- Which project is the source of truth
- Which version has been approved
- Who should review an artifact
- Whether a comment becomes future context
- How external collaborators are contained
- Which actions require approval
- How work transfers between different agents
- What the team should preserve after the agent session ends
Those are product and workflow decisions.
Why the distinction matters
Imagine that Claude, Codex, and ChatGPT can all connect to the same cloud drive through MCP.
Technically, they can access files. Operationally, the team may still experience:
- Duplicate drafts
- Agents editing the wrong version
- Feedback trapped in another application
- No explicit approved state
- Broad permissions inherited from one user
- Unclear ownership of generated work
Connectivity is necessary. Coordination is separate, and it is the job of an AI agent shared workspace.
A comparison
| Question | MCP | Shared agent workspace |
|---|---|---|
| How does an agent call a tool? | Defines a standard interface | Uses MCP, APIs, or native integrations |
| Where do project artifacts live? | Not prescribed | Persistent project structure |
| How are permissions represented? | Depends on server implementation | Product-level roles and scopes |
| How do people review output? | Not prescribed | Comments, decisions, versions, approvals |
| How does another agent continue? | Can reconnect to tools | Receives shared project context and accepted work |
| What becomes the source of truth? | Not prescribed | Defined by project workflow |
When MCP alone may be enough
MCP may be sufficient when:
- One user works with one agent
- The task is temporary
- The agent only reads information
- No other person needs to review the result
- Existing systems already handle approval and versioning
For example, asking an assistant to search personal notes may not require a new workspace.
When a shared workspace becomes necessary
A workspace becomes more valuable when:
- Multiple agents create or modify artifacts
- Work survives across many sessions
- Nontechnical teammates need to review output
- Clients or external partners participate
- Permissions must be scoped to a project
- Approved outputs need to become future context
- The team needs a visible history of changes and decisions
MCP should be an access layer, not the product story
For many users, “we have an MCP server” is not a meaningful value proposition.
Users care about outcomes:
- My teammate can review what the agent made
- My client sees only the relevant project
- The agent can address comments directly
- The next agent starts from the approved version
- I can revoke access without losing the work
MCP can enable these outcomes, but the workflow is what creates value.
The permission question
An MCP connection should not become a shortcut around authorization.
A robust implementation should consider:
- User identity
- Organization and project scope
- Read versus write capability
- Revocable tokens
- Rate limits
- Audit records
- Approval for consequential actions
The server should enforce the permission at execution time rather than trusting the agent’s prompt.
A practical architecture
A team might use:
- Claude Code for execution
- An MCP server for controlled workspace actions
- A shared project for durable files and decisions
- Human comments for review at defined checkpoints
- An approval state to identify trusted output
- A second agent that continues using the accepted context
Each layer solves a different problem.
How Agent Continuity combines the two
Agent Continuity supports agent access through integrations, APIs, and an open skills layer while preserving project files, human feedback, permissions, and collaboration in one shared environment.
The protocol allows agents to act. The workspace makes those actions useful to a team. Agent Continuity is a product of Continuity (CONT’D).