Model Context Protocol (MCP) is an open standard for connecting language models to external tools and data sources in a secure, auditable way. MCP makes it easier to integrate AI agents with APIs, databases, or automation workflows while keeping human oversight in the loop.
- Unified interface: Tools expose capabilities through standard schemas, so models can discover and use them consistently.
- Security first: Every tool call is brokered through an MCP server, allowing granular permissioning, logging, and revocation.
- Composable tooling: MCP servers can aggregate multiple tools, enabling rich agent behaviors without hard-coding integrations.
- Auditability: Requests and responses can be logged for compliance and debugging, helping teams trust model-driven automations.
- MCP Server: Hosts one or more tools and manages authentication, rate limits, and observability.
- Tool: A callable action with structured inputs/outputs (e.g., run a search query, submit a ticket, trigger a build).
- Schema: JSON schema definitions describe the tool parameters and responses so models can reason about valid calls.
- Session: Conversation state that tracks activated tools, resource handles, and human approvals when required.
- Pick or build an MCP server that wraps the APIs or automations you want to expose.
- Register the server in your model runtime (e.g., Cursor, Claude, or custom agent frameworks) with appropriate credentials.
- Test tool calls in a sandbox environment and review logs to ensure guardrails behave as expected.
- Iterate on tool schemas and authorization policies as you expand capabilities.
This repository will eventually host demos and resources for experimenting with MCP-enabled agents.