Why MCP Exists
MCP exists because AI apps and tools were multiplying faster than anyone could wire them together — a shared protocol turns N apps × M systems into N + M, not N × M.
Explain like I'm new to AI
Suppose four AI apps each need GitHub, Slack, Postgres, and Drive. That's 16 custom connectors. Add one more app or one more system and the work explodes. Users also suffer: "this tool works in Cursor but not in Claude."
MCP's job is to make that grid collapse. Each system publishes one MCP server. Each app speaks one protocol. New hosts get old servers for free; new servers show up in old hosts.
Every app needs a custom connector to every system — 4 apps × 4 systems = 16 one-off integrations.
| GitHub | Slack | Postgres | Drive | |
|---|---|---|---|---|
| Cursor | × | × | × | × |
| Claude | × | × | × | × |
| ChatGPT | × | × | × | × |
| Your agent | × | × | × | × |
Each system publishes one MCP server. Any MCP-speaking host can use it — write once, plug in everywhere.
Mental model
It's the same reason we have USB, HTTP, and SMTP. Nobody wants a unique cable, a unique web, or a unique email dialect per vendor. Shared protocols create ecosystems. MCP is that bet for "how models touch the rest of the computer."
How it works — the problem it solves
Three pressures made a protocol necessary:
- Hosts multiplied — IDEs, chat apps, internal agents, Slack bots. Each wanted the same tools.
- Tools multiplied — every SaaS wanted "works with AI." Custom plugins per host didn't scale.
- Security and identity needed one shape — OAuth, least privilege, and audit are easier when every connection looks the same.
A protocol also lets the system vendor own the connector (GitHub writes the GitHub server) instead of every app vendor re-implementing GitHub's API poorly.
Real-world example
Linear, Sentry, Figma, GitHub, and thousands of internal tools now ship MCP servers. A company can point Cursor, Claude, and an internal agent at the same servers — one identity policy, one tool catalog, three surfaces. That is the N+M world MCP was built for.
Technical explanation
The N×M problem is an ecosystem problem, not a model problem. Function calling solved "how does this model request this function in this app." It did not solve "how does any host discover any vendor's tools, with auth and a stable schema."
MCP adds:
- A capability catalog (tools / resources / prompts) any client can list.
- Transports so local (stdio) and remote (HTTP) servers share one mental model.
- Auth so remote servers aren't "paste an API key in a config file" forever.
- Stability — a deprecation window (twelve months) so hosts and servers can upgrade on purpose.
The 2026-07-28 stateless core exists for the same reason HTTP stayed stateless: N×M at runtime (sticky sessions, handshake state) was blocking scale.
Common mistakes
Building a private "our-company protocol" that only your agent speaks. You re-create the N×M problem the moment a second host (an IDE, a chat app) needs the same tools.
- Wrapping every tiny internal function as an MCP server — overhead without ecosystem benefit.
- Expecting MCP to make a bad API good. A confusing tool schema is still confusing over MCP.
When to use it
- Capabilities that should work in more than one host, or that a vendor should maintain once.
When NOT to use it
- A throwaway tool used by a single script in a single app.
Alternatives
- Per-host plugins; wrapping OpenAPI yourself; a central "tool gateway" that is secretly just MCP with extra steps.
Quick quiz
Related concepts
- What is MCP? — MCP is an open protocol that lets any AI app discover and use tools, files, and prompts from external systems through a shared client-server language.
- MCP + Agents, IDEs, and Products — MCP is the plug; agents, IDEs, and products are hosts that decide when to call — a product can also publish a server so other hosts reach it.
Further reading
Last reviewed: 2026-09-04 · Written by ByHeart AI · Reviewed by ByHeart AI