| Purpose | Expose tools, resources, and prompts to compatible AI applications | Expose service-specific operations and data to software | MCP is an AI integration protocol; APIs remain the underlying service interface in many implementations. |
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| Interaction | A host discovers server capabilities and lets a model or user invoke them within configured permissions | Application code calls documented endpoints or libraries | MCP standardizes discovery and tool use; APIs offer direct programmatic control. |
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| Typical fit | Giving compatible assistants a reusable tool surface across clients | Building deterministic product features, syncs, and backend integrations | Choose based on the consumer. Many systems use an API integration behind an MCP server. |
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| Implementation | Define protocol capabilities, schemas, transport, lifecycle, permissions, and error behavior | Implement authentication, endpoint calls, data mapping, retries, and service-specific errors | Neither removes security or reliability work. |
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| AI dependency | Designed for an MCP host, commonly an AI application | Works with ordinary software and does not require a model | Use a direct API when conversational or model-driven tool discovery is unnecessary. |
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| Cost | Depends on tools, data sources, authentication, hosting, testing, and client support | Depends on API scope, vendor access, data mapping, volume, and reliability requirements | Scope determines cost; protocol choice alone does not produce a reliable price range. |
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