Model Context Protocol (MCP)
What is Model Context Protocol (MCP)?
- An open standard for connecting LLMs to external tools and data
- Defines how a model can discover, call, and interact with tools
- Makes tool use interoperable across different apps and providers
Why MCP?
- Without MCP: each app or provider defines its own tool API format
- With MCP: one protocol → tools can work with any LLM that supports MCP
- Reduces friction for developers (no custom glue code for every integration)
MCP vs RAG
- RAG: retrieval pipeline (vector DB, search, unstructured docs)
- MCP: standard way to connect to any tool (databases, APIs, search engines, etc.)
- Together: RAG retrieval itself could be exposed as a tool via MCP
Benefits of MCP
- Portable: one tool, many LLM hosts
- Extensible: supports structured inputs/outputs
- Secure: standardizes permissions and auditing
- Future-proof: ecosystem of reusable MCP tools
Example MCP Use Case
- LLM is asked: “What’s the latest issue assigned to me in GitHub?”
- Model calls the GitHub MCP tool
- Tool fetches trusted data via API
- Model responds with the result — no custom integration required