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Model Context Protocol

An open standard for connecting models to tools and data — solving a real integration problem, and a standards fight is a political event, not a technical one.

Reading level: Curious
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When not to use it

  • For one integration. A protocol is overhead until you have several.
  • As a security boundary. It standardises connection, not permission. Your authorisation lives in your code.
  • When connecting untrusted data to a capable model. The protocol makes this easy, which is not the same as safe.
  • When the ecosystem doesn't have what you need. Then it's a spec you're implementing alone.

Reach for something else instead

  • Direct function calling — for a handful of tools you own, this is simpler.
  • Provider-specific tool APIs — less portable, fewer moving parts.
  • A plain HTTP API — if only your app calls it, the protocol buys you nothing.

Sources & further reading

  • Anthropic (2024), Model Context Protocol specification — the primary source; read the spec rather than the coverage.
  • Greshake et al. (2023), Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection — why every connected data source is an injection surface.
  • Microsoft (2016), Language Server Protocol — the precedent; the same M×N problem, solved the same way.

Primary sources, listed so you can check the claims on this page rather than take them on trust.

Where people go wrong

  • Treating the protocol as a permission system. It isn't one and doesn't claim to be.
  • Connecting a server that reads external content to a model with write capabilities, without thinking about injection.
  • Installing servers from an ecosystem with the trust assumptions you'd apply to a signed release. It's a package ecosystem.
  • Assuming standardisation implies safety. It implies convenience, on both sides.

At a glance

FieldAI Agents
ProblemM×N tool integrations
Shapehost, client, server; JSON-RPC over stdio or HTTP
Three primitivestools (model-controlled), resources (app-controlled), prompts (user-controlled)
Nota security model
PrecedentLSP
DifficultyIntermediate
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Often compared with

MCP vs. provider tool APIs — one protocol many clients speak, versus a schema per vendor. The trade is portability against fewer moving parts.

Related concepts

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Tool Use

Letting a model call real software — a search, a calculator, your database — instead of trying to answer everything from memory.

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Function Calling

How a model asks your code to do something — the mechanism underneath every agent, and it's the model requesting, never executing.

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AI Agent

Software that pursues a goal by taking its own steps — deciding, acting, and reacting — instead of answering once and stopping.

Safety & Ethics

Prompt Injection

Hiding instructions inside content a model reads, so it follows the attacker instead of you — and the reason is structural, which is why it isn't fixed.

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Guardrails

The checks around a model that decide what it's allowed to receive, say, and do — the part that stops a demo becoming an incident.

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Agent Memory

Giving an AI a way to remember across conversations, since the model itself forgets everything the moment a session ends.