Writing on AI research, product thinking, and system architecture
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Model Context Protocol (MCP) is powerful — and frequently misunderstood.
Teams often confuse:
- MCP vs RAG
- MCP vs plugins
- MCP vs traditional APIs
As a result, MCP is either:
- overused where it adds complexity, or
- underused where it could simplify system design.
The problem is not tooling.
It is the mental model.
MCP changes how context and capabilities are exposed, not just how models are called.
This creates confusion because:
- MCP operates at the boundary between model and system
- responsibilities shift across components
- security, permissions, and context become intertwined
- benefits are architectural, not immediately visible
Without a clear mental model, teams make poor design decisions early.
A plain-language mental model for understanding MCP from a product and system perspective.
It explains:
- what MCP actually enables
- what problems it is meant to solve
- when MCP is the right abstraction
- when it adds unnecessary complexity
This is an AI product design guide, not an implementation tutorial.
MCP defines how models access context and capabilities — not what they do with them.
Key idea:
- MCP externalizes context and tools
- the model remains stateless
- the system controls exposure, scope, and permissions
MCP is about boundaries, not intelligence.
MCP works best when you need:
- standardized context access
- controlled tool exposure
- clear separation between model and system
- consistent behavior across models
- centralized permission management
It shines in multi-tool, multi-model environments.
MCP is not ideal when:
- the system is simple or single-purpose
- context is static and small
- tool usage is minimal
- architectural overhead outweighs benefits
MCP does not automatically improve reasoning, accuracy, or reliability.
- "MCP replaces RAG"
- "MCP makes models smarter"
- "MCP is just a plugin system"
- "MCP is required for agents"
MCP is an interface and control layer, not a capability upgrade.
Incorrect mental models lead to:
- over-engineering
- security risks
- leaky context boundaries
- brittle systems
- hard-to-debug failures
Correct mental models lead to:
- clearer system boundaries
- safer tool access
- easier evolution over time
Most MCP problems are design problems, not protocol problems.
This mental model is typically used:
- before adopting MCP
- during system architecture design
- when scaling tool ecosystems
- when introducing multiple models
- during security and permission reviews
It provides a shared understanding across product, engineering, and research teams.
- Not an MCP tutorial
- Not a protocol specification
- Not an implementation guide
- Not a best-practices checklist
It is a thinking framework, not documentation.
Applying this mental model encourages teams to:
- design explicit context boundaries
- treat tools as capabilities, not extensions
- centralize permissions
- keep models stateless and replaceable
Good MCP systems feel boring and predictable — by design.
- 🟢 Conceptually stable
- 🟡 Examples intentionally omitted
- 🔒 Deeper implementations are intentionally excluded by design
MCP doesn’t change what models can think.
It changes what they are allowed to see and do.