Prompt Surfaces in Model Context Protocol (MCP) Prompt surfaces in MCP refer to the specific points in an application where user inputs, system instructions, or contextual data are formatted and delivered to a language model. These surfaces act as interfaces that shape how the model interprets requests, ensuring consistency and control over its outputs. For example, a chatbot might have distinct surfaces for handling user queries, injecting system-level rules, and incorporating conversation history. Each surface defines how information is structured (e.g., text templates, JSON payloads) and what constraints apply (e.g., input length, allowed topics). The goal is to reduce ambiguity in model inputs while maintaining flexibility for different use cases.
Implementation Guidelines
To implement prompt surfaces in MCP, start by identifying all interaction points where the model receives input. For a customer support bot, this could include the user’s message, a knowledge base snippet, and a system prompt like “Respond politely and avoid technical jargon.” Structure each surface using templates or schemas. For instance, use a JSON object with fields like user_query, context, and system_instruction, each with validation rules (e.g., character limits, allowed keywords). Tools like JSON Schema or custom validators ensure inputs adhere to these rules before reaching the model. Additionally, separate surfaces for different tasks (e.g., summarization vs. Q&A) prevent unintended behavior by isolating context and instructions.
Examples and Best Practices
A practical example is a code-generation tool where one surface handles the user’s code request, another injects security guidelines (e.g., "Avoid using eval()"), and a third appends recent error logs for context. Use versioned templates to iterate on prompts without breaking existing integrations. For testing, simulate edge cases like overly vague inputs or adversarial prompts to ensure surfaces handle them gracefully. Tools like A/B testing frameworks or monitoring dashboards help track performance across surfaces. Avoid hardcoding values; instead, use configuration files or environment variables to manage templates, making updates easier. By isolating and validating each surface, you maintain control over model behavior while scaling to complex workflows.