MCP for teams
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.
Once you have decided to connect your AI tools over MCP, the next fork is where the server runs. Local, on your own machine, or remote, hosted at a URL. They speak the same protocol, but they suit different tools, and picking wrong leaves half your stack out of the loop.
Here is the practical breakdown, and why the real answer for a team is usually "both."
The core difference
A local MCP server runs on your own machine. The AI tool talks to it over a local channel, no network hop. A remote MCP server is hosted somewhere and reached over the network at a URL. That is the whole distinction, and everything else follows from it. If the underlying protocol is still fuzzy, what is MCP covers the client/server basics.
| Local MCP server | Remote MCP server | |
|---|---|---|
| Where it runs | On your machine | Hosted, reachable at a URL |
| Reachable from | Your device only | Anywhere on the network |
| Works offline | Yes | No, needs the network |
| Latency | Very low, no network hop | Network round trip |
| Suits | Desktop tools (Claude Code, Cursor) | Web tools, hosted agents, ChatGPT |
| Team access | Per machine | One endpoint for everyone |
When local wins
Reach for a local server when the tool lives on your machine and latency or offline use matters:
- Desktop coding tools. Claude Code, Cursor, and Windsurf run on your machine, so a local bridge is the lowest-latency path, with no network round trip on every read.
- You work offline sometimes. A plane, a bad connection, a locked-down network. A local server keeps working when the network does not.
- You want data to stay on the device. For desktop work, a local bridge keeps that traffic on the machine rather than crossing the network.
The cost is reach. A local server is invisible to anything that is not on that exact machine.
When remote wins
Reach for a remote endpoint when the tool is not on your machine, or when other people need in:
- Web tools. ChatGPT and other browser-based assistants run in the cloud. They cannot see a server on your laptop. A hosted URL is the only way they read your context. This is the single biggest reason local-only setups fall short.
- Hosted agents. An agent running in the cloud needs a reachable endpoint, not a local socket.
- Team-wide access. One hosted endpoint is reachable by everyone, independent of whether any one person's machine is on.
- Multiple devices. The same context follows you across machines with nothing to reinstall.
We go deeper on the hosted side in what is a remote MCP server.
On security, briefly
A common assumption is that local is automatically the safe choice. It is more nuanced. A local server keeps data off the network but is only as safe as the machine it runs on, and it gives a team no central control. A well-run remote endpoint authenticates and encrypts every connection and can be more auditable than a scatter of local setups. The deciding factors are authentication, encryption, and access control, not local versus remote on its own. Full treatment in is MCP secure.
Why a team wants both
The trap is treating this as an either/or. It is not. A coding-heavy team that goes local-only locks out everyone on a web tool. A team that goes remote-only gives up the low-latency, offline desktop path their engineers want.
The thing that makes "both" work is that both point at one shared context. If your local bridge and your remote endpoint serve different sources, you are back to silos, just over two transports instead of one.
This is how BaseThread is built. The local bridge ships inside a native Mac app and serves desktop tools at low latency. The remote endpoint at mcp.basethread.ai serves web tools and hosted agents. Both read the same curated context graph, your company, products, teams, projects, and your own area, plus the same running record of activity, decisions, and tasks. So a teammate in Cursor and a teammate in ChatGPT read the same facts, and the integrations that distill context from tools like Notion and HubSpot feed both paths. Surface stops mattering, which is the point. See how it works.
The rule of thumb
Desktop coding tool? Local bridge. Web tool or hosted agent? Remote endpoint. The only mistake is pointing them at different context instead of one shared source.
TL;DR
A local MCP server runs on your machine, low-latency and offline-capable, and suits desktop coding tools. A remote MCP server is hosted at a URL, reachable from anywhere, and is the only way web tools and hosted agents can read your context. Neither is universally better, and security comes down to authentication and access control rather than local versus remote. Teams want both, and the thing that makes both work is pointing them at one shared source, which is how BaseThread's local bridge and remote endpoint serve the same curated context.
Local bridge for desktop tools, remote endpoint for web tools, one shared context behind both.
Related reading
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.
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.
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.
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.
Frequently asked questions
What is the difference between a local and a remote MCP server?
A local MCP server runs on your own machine and the AI tool talks to it over a local channel, so it works offline and keeps data on the device. A remote MCP server is hosted and reached over the network at a URL, so it can serve web tools, hosted agents, and a whole team from anywhere. Both speak the same protocol; they differ in where the server runs and who can reach it.
Which is better, local or remote MCP?
Neither is better in general; they suit different tools. Local is the better fit for desktop coding tools like Claude Code and Cursor because it is low-latency and works offline. Remote is the better fit for web tools and hosted agents that cannot reach a server on someone's laptop. Most teams use both, pointed at the same shared context.
Can a web tool like ChatGPT use a local MCP server?
No. A web-based tool runs in the cloud and cannot reach a server running only on your machine. Web tools and hosted agents need a remote MCP endpoint, a hosted URL they connect to over the network. That is the main reason a local-only setup leaves half your tools out.
Do local and remote MCP servers serve different context?
They do not have to, and ideally they do not. With BaseThread the local Mac bridge and the remote endpoint serve the same curated context and the same activity, decisions, and tasks record, so a teammate in Cursor and a teammate in ChatGPT read the same source regardless of surface.