DEVELOPER GUIDES
Implementation guidance for capability discovery, remote MCP connections, credential isolation, runtime permissions, structured execution, and verification across business applications.
An AI agent integration gives a model a structured, permissioned way to discover and execute software capabilities. Unlike a fixed automation, the agent can choose an action based on the task. A production implementation therefore needs more than an API wrapper: it needs discoverable schemas, connection identity, least-privilege authorization, runtime validation, structured results, and an execution record.
READ GUIDEIn AI, MCP means Model Context Protocol: a standard way for an AI client to discover and call external tools. A remote MCP integration can expose a small discovery surface while keeping provider credentials in a separate runtime. The agent receives action schemas and results, but OAuth tokens and API keys do not need to be inserted into prompts or stored in the agent process.
READ GUIDESecure agent execution separates model reasoning from credential storage and policy enforcement. The model may propose or invoke an action, but a trusted runtime must validate the schema, connection identity, permissions, destination, and policy before contacting the provider. The result should be redacted, structured, and tied to an execution record that operators can inspect.
READ GUIDEMCP and HTTP APIs solve different layers of an agent integration. MCP standardizes how an AI host discovers capabilities and invokes tools during an agent session. An HTTP API exposes explicit endpoints that application code calls directly. Use MCP when the agent client should discover and select tools; use an API when your product owns orchestration and needs deterministic request control. A production application layer can expose both over the same identities, permissions, and execution runtime.
READ GUIDEThe best MCP server for Claude Code is the smallest trustworthy capability set that completes the engineering task. Start from the work—repository changes, issue management, incident investigation, deployment checks, or team communication—then evaluate server provenance, authorization, tool contracts, permission scope, context cost, failure behavior, and execution evidence. Popularity alone does not establish safety or reliability.
READ GUIDEAn MCP server list is useful only when it helps an agent complete a real task safely. Start with the system of record and the smallest capability set: GitHub for repository work, Gmail for bounded email workflows, Google Calendar for scheduling, Slack for team communication, and CRM or issue-tracking software for customer and project context. Before installation, verify server provenance, authorization, permission scope, tool contracts, and how the result can be checked.
READ GUIDEA local MCP server runs beside the client and commonly communicates over standard input and output. A remote MCP server runs as a network service and commonly uses Streamable HTTP. Local deployment gives the operator direct control of the process and environment; remote deployment centralizes updates, authorization, policy, and availability. The correct choice depends on who operates the server, where credentials must live, and whether many users or agents need one managed endpoint.
READ GUIDEONE CONNECTION. REAL WORK.
Connect through MCP or explore the Agent App catalog and choose only the capabilities your workflow needs.
