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collaborativereasoning

Simulate expert collaboration to tackle complex problems by coordinating diverse perspectives and integrating insights through structured reasoning frameworks.

Instructions

A detailed tool for simulating expert collaboration with diverse perspectives. This tool helps models tackle complex problems by coordinating multiple viewpoints. It provides a framework for structured collaborative reasoning and perspective integration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
personasYes
contributionsYes
stageYes
activePersonaIdYes
nextPersonaIdNo
consensusPointsNo
disagreementsNo
keyInsightsNo
openQuestionsNo
finalRecommendationNo
sessionIdYesUnique identifier for this collaboration session
iterationYesCurrent iteration of the collaboration
suggestedContributionTypesNo
nextContributionNeededYesWhether another contribution is needed

Implementation Reference

  • Implementation of the CollaborativeReasoningServer class which handles processing the collaborative reasoning task.
    export class CollaborativeReasoningServer {
      private validateInputData(input: unknown): CollaborativeReasoningData {
        const data = input as CollaborativeReasoningData;
        if (!data.topic || !data.personas || !data.contributions || !data.stage || !data.activePersonaId || !data.sessionId) {
          throw new Error("Invalid input for CollaborativeReasoning: Missing required fields.");
        }
        if (typeof data.iteration !== 'number' || data.iteration < 0) {
            throw new Error("Invalid iteration value for CollaborativeReasoningData.");
        }
        if (typeof data.nextContributionNeeded !== 'boolean') {
            throw new Error("Invalid nextContributionNeeded value for CollaborativeReasoningData.");
        }
        return data;
      }
    
      private formatOutput(data: CollaborativeReasoningData): string {
        const { topic, personas, contributions, stage, activePersonaId, iteration } = data;
        
        let output = `\n${chalk.bold.blue('Collaborative Reasoning Session')}\n`;
        output += `${chalk.bold.green('Topic:')} ${topic}\n`;
        output += `${chalk.bold.yellow('Stage:')} ${stage} (Iteration: ${iteration})\n`;
        
        // Active persona
        const activePersona = personas.find(p => p.id === activePersonaId);
        if (activePersona) {
          output += `\n${chalk.bold.magenta('Active Persona:')} ${activePersona.name}\n`;
          output += `${chalk.bold.cyan('Expertise:')} ${activePersona.expertise.join(', ')}\n`;
          output += `${chalk.bold.cyan('Perspective:')} ${activePersona.perspective}\n`;
        }
        
        // Contributions
        if (contributions.length > 0) {
          output += `\n${chalk.bold.green('Contributions:')}\n`;
          
          for (const contribution of contributions) {
            const persona = personas.find(p => p.id === contribution.personaId);
            const personaName = persona ? persona.name : contribution.personaId;
            
            output += `${chalk.bold(`${personaName} (${contribution.type}, confidence: ${contribution.confidence.toFixed(2)}):`)} `;
            output += `${contribution.content}\n\n`;
          }
        }
        
        // Consensus points
        if (data.consensusPoints && data.consensusPoints.length > 0) {
          output += `\n${chalk.bold.green('Consensus Points:')}\n`;
          data.consensusPoints.forEach((point, i) => {
            output += `${chalk.bold(`${i+1}.`)} ${point}\n`;
          });
        }
        
        // Key insights
        if (data.keyInsights && data.keyInsights.length > 0) {
          output += `\n${chalk.bold.yellow('Key Insights:')}\n`;
          data.keyInsights.forEach((insight, i) => {
            output += `${chalk.bold(`${i+1}.`)} ${insight}\n`;
          });
        }
        
        // Final recommendation
        if (data.finalRecommendation) {
          output += `\n${chalk.bold.cyan('Final Recommendation:')}\n${data.finalRecommendation}\n`;
        }
        
        return output;
      }
    
      public processCollaborativeReasoning(input: unknown): CollaborativeReasoningData {
        const validatedData = this.validateInputData(input);
        
        // Log formatted output to console
        const formattedOutput = this.formatOutput(validatedData);
        console.error(formattedOutput);
        
        return validatedData;
      }
    }
  • The tool registration handler in src/index.ts that invokes the collaborativeReasoningServer instance.
    case "collaborativereasoning": {
        const result =
            collaborativeReasoningServer.processCollaborativeReasoning(
                request.params.arguments
            );
        return {
            content: [
                {
                    type: "text",
                    text: JSON.stringify(result, null, 2),
                },
            ],
        };
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, and description discloses no behavioral traits. Fails to explain the iterative workflow (implied by iteration/sessionId parameters), what the tool returns (suggested next persona? full dialogue?), state persistence, or side effects. Agent cannot infer operational semantics from description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences of moderate length. Burdened by filler phrases ('A detailed tool,' 'framework for') that restate the tool category without adding specificity. Not overly verbose, but lacks informational density expected for a complex multi-step reasoning tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Inadequate for a 15-parameter tool managing stateful collaborative sessions. No output schema is provided, yet description doesn't explain return values. Omits explanation of the collaborative workflow (turn-taking, consensus building) and how the 8 required parameters interact.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is critically low at 20% (only sessionId and iteration documented). Description minimally compensates by referencing 'expert collaboration' and 'viewpoints,' loosely hinting at personas/contributions, but fails to explain the complex stage lifecycle, contribution types enum, or nested structures like disagreements/consensusPoints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States the tool simulates 'expert collaboration with diverse perspectives' and 'coordinating multiple viewpoints,' which clarifies the general domain. However, it fails to distinguish from sibling reasoning tools like structuredargumentation, decisionframework, or sequentialthinking, leaving ambiguity about when to select this specific approach.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides no guidance on when to use this tool versus the ten sibling reasoning tools. No mention of prerequisites (e.g., needing predefined personas), when to iterate versus terminate, or alternative approaches for the same problem type.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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