Collaborative Reasoning MCP Server

Motivation

Complex problems often benefit from diverse perspectives and expertise. While language models can attempt to simulate different viewpoints, they often:

1. Fail to maintain consistent, distinct perspectives throughout an analysis
2. Struggle to create genuine productive tension between viewpoints
3. Blend different expertise domains in unrealistic ways
4. Neglect to systematically integrate insights from different perspectives
5. Miss opportunities for creative synthesis that emerges from diverse thinking

The Collaborative Reasoning Server addresses these limitations by creating a structured environment for multiple simulated experts to collaborate on complex problems. By externalizing collaborative thinking, models can leverage diverse perspectives more systematically and effectively.

Technical Specification

Tool Interface

interface Persona {
  id: string;
  name: string;
  expertise: string[];
  background: string;
  perspective: string;
  biases: string[];
  communication: {
    style: string;
    tone: string;
  };
}

interface Contribution {
  personaId: string;
  content: string;
  type: "observation" | "question" | "insight" | "concern" | "suggestion" | "challenge" | "synthesis";
  referencesIds?: string[]; // IDs of previous contributions this builds upon
  confidence: number; // 0.0-1.0
}

interface Disagreement {
  topic: string;
  positions: Array<{
    personaId: string;
    position: string;
    arguments: string[];
  }>;
  resolution?: {
    type: "consensus" | "compromise" | "integration" | "tabled";
    description: string;
  };
}

interface CollaborativeReasoningData {
  // Core collaboration components
  topic: string;
  personas: Persona[];
  contributions: Contribution[];
  disagreements?: Disagreement[];
  
  // Process structure
  stage: "problem-definition" | "ideation" | "critique" | "integration" | "decision" | "reflection";
  activePersonaId: string;
  nextPersonaId?: string;
  
  // Collaboration output
  keyInsights?: string[];
  consensusPoints?: string[];
  openQuestions?: string[];
  finalRecommendation?: string;
  
  // Process metadata
  sessionId: string;
  iteration: number;
  
  // Next steps
  nextContributionNeeded: boolean;
  suggestedContributionTypes?: string[];
}

Process Flow

sequenceDiagram
    participant Model
    participant ColServer as Collaborative Reasoning Server
    participant State as Collaboration State
    
    Model->>ColServer: Define problem and personas
    ColServer->>State: Initialize collaboration with personas
    ColServer-->>Model: Return initial state with first active persona
    
    Model->>ColServer: Submit contribution as Persona A
    ColServer->>State: Store contribution
    ColServer-->>Model: Return updated state with next persona prompt
    
    Model->>ColServer: Submit contribution as Persona B
    ColServer->>State: Store contribution, identify disagreement
    ColServer-->>Model: Return updated state highlighting disagreement
    
    Model->>ColServer: Submit contribution as Persona C addressing disagreement
    ColServer->>State: Store contribution, update disagreement status
    ColServer-->>Model: Return updated state
    
    Model->>ColServer: Submit synthesis contribution integrating perspectives
    ColServer->>State: Store synthesis, update consensus points
    ColServer-->>Model: Return updated state
    
    Model->>ColServer: Generate final recommendation
    ColServer->>State: Store recommendation with multi-perspective justification
    ColServer-->>Model: Return final collaboration output

Key Features

1. Multi-Persona Simulation

The server enables creation and management of diverse personas:

• Expertise profiles: Defined knowledge domains
• Perspectives: Unique viewpoints and priorities
• Communication styles: Consistent voice for each persona
• Explicit biases: Acknowledged limitations in each perspective

2. Structured Collaboration Process

The server guides a systematic collaborative process:

• Problem definition: Framing the challenge from multiple perspectives
• Ideation: Generating diverse approaches
• Critique: Systematic evaluation from different viewpoints
• Integration: Synthesizing insights across perspectives
• Decision: Reaching reasoned conclusions

3. Disagreement Management

The server provides mechanisms for productive disagreement:

• Disagreement tracking: Explicitly documenting differing views
• Position mapping: Clarifying where perspectives diverge
• Resolution strategies: Methods for handling disagreements
• Structured debate: Focused exploration of key differences

4. Cross-Pollination Tracking

The server tracks how ideas evolve across personas:

• Reference links: How contributions build on each other
• Insight evolution: Tracing how ideas transform
• Integration patterns: How diverse inputs combine

5. Visual Representation

The server visualizes the collaborative process:

• Contribution networks showing idea evolution
• Perspective maps highlighting agreements and disagreements
• Synthesis visualizations showing integrated insights

Usage Examples

Complex Problem Solving

For multifaceted problems, the model can simulate perspectives from different domains (technical, ethical, business, legal) to develop comprehensive solutions.

Product Design

When designing products, the model can incorporate perspectives of engineers, designers, marketers, and users to identify optimal approaches.

Strategic Planning

For organizational strategy, the model can simulate perspectives of different stakeholders and departments to create more robust plans.

Ethical Dilemma Analysis

When analyzing ethical questions, the model can represent diverse philosophical, cultural, and stakeholder perspectives.

Implementation

The server is implemented using TypeScript with:

• A core CollaborativeReasoningServer class
• Persona management system
• Contribution and disagreement tracking
• Facilitation algorithms to guide productive collaboration
• Standard MCP server connection via stdin/stdout

This server enhances model capabilities for complex problems requiring diverse expertise and perspectives, allowing for more thorough exploration of solution spaces and more robust final recommendations.