Enhancement: Add thought-level completions capability

[mettamatt]

Overview

Investigate expanding the existing completions capability beyond prompt arguments to include intelligent thought-level completions for the code-reasoning tool.

Current State

• ✅ Completions capability is already enabled (completions: {} in server capabilities)
• ✅ CompleteRequestSchema handler exists for prompt argument completions
• ✅ Infrastructure in place with thoughtHistory and branches tracking

Proposed Enhancement

Add intelligent completions for the thought parameter in the code-reasoning tool to help users:

1. Context-Aware Suggestions

• Analyze thoughtHistory to suggest logical next steps
• Detect reasoning patterns and suggest continuations
• Provide programming-specific thought templates

2. Pattern-Based Completions

Based on partial input, suggest completions for common patterns:

• Debugging: "debugging this by examining...", "debugging step-by-step: first check..."
• Algorithm Analysis: "analyzing the time complexity...", "considering alternative algorithms..."
• Code Review: "reviewing this code for...", "potential issues I see..."
• System Design: "breaking this system into components...", "considering scalability..."

3. Structured Reasoning Templates

Auto-suggest based on reasoning context:

• Branching: "Let me explore an alternative approach: "
• Revision: "I need to revise my earlier thinking about "
• Analysis: "Breaking this down into components: "

Technical Implementation Ideas

// Extend existing CompleteRequestSchema handler
if (req.params.ref.type === 'ref/tool' && req.params.ref.name === 'code-reasoning') {
  const argName = req.params.argument.name;
  if (argName === 'thought') {
    return await this.getThoughtCompletions(req.params.argument.value);
  }
}

private getThoughtCompletions(partialThought: string): CompletionResult {
  // Analyze thought history, detect patterns, suggest continuations
}

Benefits

• Improved UX: Helps users formulate better reasoning chains
• Pattern Recognition: Guides users toward effective problem-solving approaches
• Consistency: Encourages following the tool's "CRITICAL CHECKLIST"
• Productivity: Reduces cognitive load for complex programming analysis

Investigation Tasks

• Research MCP completion patterns for tool parameters (not just prompts)
• Design completion suggestion algorithms based on thought history
• Create programming-specific reasoning pattern templates
• Prototype implementation and test with real scenarios
• Evaluate impact on reasoning quality and user experience

Related

This builds on the existing completions infrastructure introduced in MCP SDK 1.12.3 (fixed in the current dependency update PR #19).

[BradKML]

Sorry for contacting from another repo, but the idea of a reasoning MCP sounds interesting. What are the tools to borrow from other libraries when it comes to code complexity and bug tracing? algorithmicsuperintelligence/openevolve#199