The sampling mechanism in Model Context Protocol (MCP) dynamically adjusts how tokens are selected during text generation by prioritizing context-aware decisions. At its core, MCP uses a combination of probability distributions and context window analysis to determine the next token. Unlike basic sampling methods that rely solely on token probabilities, MCP evaluates the current context—such as recent tokens, user intent, or domain-specific patterns—to influence sampling. For example, if the model is generating code, MCP might prioritize syntax-specific tokens (e.g., closing brackets) when the context indicates an incomplete block. This approach balances randomness and determinism by tuning parameters like temperature or top-k thresholds based on the context’s needs.
To illustrate, consider a scenario where MCP is used for a chatbot. When the conversation context includes technical terms like “API integration,” MCP might lower the sampling temperature to reduce randomness, ensuring responses stay focused on technical accuracy. Conversely, in a casual dialogue about movies, it could increase temperature to allow more creative suggestions. Additionally, MCP might segment the context into “local” (recent tokens) and “global” (entire conversation history) layers. For instance, if a user repeatedly corrects a term (e.g., “Not Java, JavaScript”), MCP could boost the probability of “JavaScript” in subsequent responses by analyzing the global context. These adjustments happen in real time, enabling the model to adapt without manual intervention.
Coherence is maintained through constraints that align sampling with the context’s logical flow. For example, MCP might track entity consistency (e.g., ensuring a character’s name doesn’t change mid-story) by caching key entities and their relationships. If the context includes “Alice handed the document to Bob,” MCP would downweight tokens like “she” referring to Bob to avoid ambiguity. It also handles long-range dependencies by periodically summarizing context segments, allowing the model to reference key points without recalculating the entire history. By integrating these techniques, MCP ensures generated text remains relevant and logically consistent, even in complex or evolving scenarios.