Streamlining MCP Handoffs: Preventing System Prompt Interference in Dyad

Building a robust AI-integrated environment like Dyad requires delicate handling of handoff protocols between the model and external tools. Recently, we encountered issues where our Model Context Protocol (MCP) interactions and system prompts were causing unexpected behavior during task execution.

The Problem: Handoff Collisions

When our system attempted to transition control to an 'edit-code' tool, the model would often continue generating tokens instead of stopping cleanly. This caused redundant processing and potentially corrupted state. Additionally, we discovered that certain reserved tags were leaking into the system prompt, giving the model opportunities to misuse internal directives.

The Implementation Strategy

To ensure a clean handoff, we implemented a explicit 'stop-generation' trigger when an 'edit-code' tool call is detected. By integrating a check within our message processing loop, we can now reliably signal the model to halt before it enters an invalid state.

function processModelOutput(output: ModelResponse): void {
  const hasEditToolCall = output.toolCalls.some(call => call.name === 'edit-code');

  if (hasEditToolCall) {
    // Signal the model to stop and yield control
    terminateGeneration();
    executeEditCode(output.toolCalls);
  }
}

Securing the System Prompt

To prevent the model from inadvertently acting on reserved internal tags, we implemented a sanitization rule for all outgoing prompts. By filtering out any inputs formatted with our internal namespace, we maintain a secure boundary between user content and system configuration.

function sanitizeSystemPrompt(prompt: string): string {
  // Block internal tags to prevent prompt injection or misuse
  const forbiddenPattern = /<dyad-.*?>/g;
  return prompt.replace(forbiddenPattern, '');
}

The Outcome

These adjustments have significantly improved the reliability of our tool handoffs. The system now respects the boundaries between generation and execution, and our system prompts are shielded from internal tag leakage. By combining these safeguards with our existing step-limit logic, we have created a much more predictable interaction flow for the user.

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