Salesforce MCP Integration Server

The Salesforce MCP Sample Integration is a ready‑to‑run Model Context Protocol server that bridges AI assistants with the Salesforce platform. By exposing a set of MCP tools, it allows an assistant to perform core Salesforce operations—such as sending email messages and deploying Apex code—directly from conversational prompts. This eliminates the need for developers to write custom API wrappers or handle OAuth flows manually, thereby accelerating the integration of Salesforce data and automation into AI workflows.

At its core, the server authenticates to Salesforce using the JWT Bearer Flow. This flow is ideal for machine‑to‑machine interactions because it relies on a signed JWT and a private key, removing the requirement for interactive login or refresh tokens. Once authenticated, the server exposes MCP tools that map to Salesforce REST endpoints: one tool sends emails via the API, while another compiles and deploys Apex classes using the Metadata API. Each tool is defined with clear input schemas, ensuring that AI assistants can validate arguments before invoking the operation.

Developers benefit from several key capabilities:

• Declarative tool definitions that expose Salesforce actions without exposing underlying REST details.
• Secure credential handling via a user‑supplied module, keeping secrets out of the repository.
• Extensibility: additional Salesforce endpoints (e.g., CRUD on objects, query execution) can be added by defining new MCP tools.
• Rich integration with Claude or other AI assistants: a user can simply say, “Send an email to the lead list” and the assistant will prompt for subject/body before calling the tool.

Typical use cases include:

• Automating lead nurturing workflows by sending personalized emails from within an AI conversation.
• Rapidly deploying code changes during a sprint when an assistant can generate Apex snippets and push them to the org.
• Enabling data‑driven decision making where an assistant queries Salesforce records and presents insights in natural language.

Because the server adheres to MCP standards, it plugs seamlessly into any AI pipeline that supports MCP. Once running, an assistant can discover the available tools via the standard endpoint and invoke them using the familiar message format. This tight coupling between conversational context and Salesforce actions makes it straightforward to build intelligent agents that can read, write, and orchestrate business logic across Salesforce—all while keeping security and scalability in focus.