MCP (Model Context Protocol) is a standard way to let an AI assistant connect to external tools and data sources through “MCP servers”.
Instead of hardcoding every integration inside your app/agent, MCP defines a consistent interface so a model can:
- Discover available tools (capabilities) exposed by a server
- Call those tools with structured inputs/outputs
- Access resources (documents, files, metadata) in a uniform way
In practice, MCP helps you plug an assistant into systems like GitHub, Jira, databases, filesystems, internal APIs, etc., in a safer and more structured manner than free-form “call an API somehow”.
Common categories:
- Source control / code hosting: GitHub (PRs, issues, diffs, checks)
- Project management: Jira (issues, transitions, comments)
- Databases: Postgres (read queries, metadata)
- Files & documents: filesystem access, Drive/Docs connectors
- Browser automation: page navigation, snapshots, form filling (useful for testing web apps)
Use MCP when:
- You need the model to interact with real systems (create issues, query a DB, fetch repo data)
- You want standardized integrations across different tools/providers
- You want a clear boundary between:
- your application logic, and
- the set of tool capabilities the model can access
Avoid MCP (or keep it out of the critical path) when:
- The task can be solved with a static prompt or offline documents
- You only need a couple of simple API calls and a direct SDK is cleaner
- You have strong constraints on latency, cost, or reliability and tool calls would add overhead
- Security requirements demand extremely tight control and auditing, and you’re not ready to govern tool access properly
- Keep tool scopes small and explicit (principle of least privilege).
- Prefer read-only tools where possible; require explicit user intent for side effects.
- Log tool calls and outputs for debugging and auditability.