Overview
The Actor-Critic Thinking MCP Server delivers dual-perspective analysis within the MCP framework to evaluate performance and decisions. It alternates between actor (empathetic/creative) and critic (analytical/evaluative) roles, tracks rounds, and combines empathetic understanding with objective assessment to produce nuanced results. The server generates multi-dimensional evaluations and provides actionable feedback for improvement. It is designed for situations requiring balancing subjective creativity with objective execution, including evaluating artistic performances, strategic decisions, and complex scenarios. The evaluation process is parameterized with fields such as content, role, nextRoundNeeded, thoughtNumber, and totalThoughts (the latter must be odd and at least 3). Use cases span performance reviews, iterative refinement of creative processes, gap analyses between intention and execution, and feedback that benefits both creators and evaluators. The server can be built and run locally (npm run build; node dist/index.js) and is compatible with MCP-based prompting and multi-round thought sequences.
Features
Dual-perspective Analysis
Alternates between actor (creator/performer) and critic (analyzer/evaluator) perspectives to balance assessment.
Round Tracking
Keeps track of successive rounds of actor-critic dialogue for traceability and depth.
Balanced Assessment
Integrates empathetic understanding with objective analysis to produce balanced evaluations.
Multi-dimensional Evaluation
Generates nuanced assessments across multiple criteria and dimensions.
Actionable Feedback
Delivers concrete, constructive recommendations for improvement.
Who Is This For?
- Creative professionals:Evaluate performances and creative works with dual actor-critic perspectives to improve outcomes.
- Evaluators:Provide rigorous, balanced feedback through technical and empathetic analysis to stakeholders.
- Managers:Oversee projects requiring balanced assessment of subjective and objective criteria.
- Researchers:Experiment with dual-perspective thinking models and study performance evaluation dynamics.
