MCP Servers
Overview
MCP servers let AI tools connect to external context and capabilities in a structured way.
Why It Matters
They can reduce manual context gathering and make AI assistance more useful in real workflows.
Core Concepts
- Servers expose tools or resources.
- The client asks for capability, not raw access.
- Permissions and trust boundaries still matter.
Mental Models
Think of MCP as a controlled adapter between the model and your tools.
Best Practices
- Expose only useful capabilities.
- Keep tool names and outputs predictable.
- Review security implications before connecting tools.
Common Mistakes
- Exposing too much capability.
- Assuming connected tools are inherently safe.
- Forgetting to document what the server does.
Trade-offs
MCP can make AI more powerful, but every connection adds another trust and maintenance boundary.
Decision Framework
| Need | MCP helps? |
|---|---|
| Access to structured tools | Yes |
| Simple prompt-only task | No |
Examples
- Connect a docs source so the model can answer from real project context.
Checklists
- Is the tool scope minimal?
- Are permissions understood?
- Is the behavior documented?
Senior Engineer Notes
Senior engineers treat tool connections as product decisions, not just convenience settings.