Actor-Critic Thinking MCP Server

What does “Actor-Critic Thinking” MCP Server do?

The Actor-Critic Thinking MCP Server is a dual-perspective analysis tool based on the Model Context Protocol (MCP). It enables AI assistants and clients to conduct comprehensive performance evaluations by alternating between the “actor” (creator or performer) and “critic” (analyzer or evaluator) roles. This approach allows for balanced assessments that combine empathetic understanding with objective analysis. The server supports nuanced, multi-dimensional evaluations and provides actionable feedback and improvement suggestions. By bridging intention and execution, it enhances development workflows, especially in scenarios where both subjective and objective criteria are important, such as creative reviews, performance assessments, and iterative refinement processes.

List of Prompts

• Actor Perspective Prompt: Guides the AI to reflect on intentions, creative choices, emotional context, challenges faced, and self-reflection from the creator’s viewpoint.
• Critic Perspective Prompt: Directs the AI to analyze technical execution, evaluate effectiveness and audience impact, provide comparative analysis, and offer objective feedback and suggestions for improvement.
• Round Tracking Prompt: Maintains context and flow of actor-critic dialogue by tracking the round and role.
• Multi-dimensional Evaluation Prompt: Ensures that assessments cover both subjective (empathy/vision) and objective (execution/impact) dimensions.

List of Resources

• Role Perspective Guidelines: Provides structured instructions for both actor and critic roles to standardize the evaluation process.
• Parameters Documentation: Details required inputs such as content, role, nextRoundNeeded, thoughtNumber, and totalThoughts for consistent interactions.
• Showcase Demonstrations: Includes visual examples (with and without actor-critic thinking) to help users understand the evaluation process.
• Improvement Suggestions: Offers actionable feedback resources based on dual-perspective analysis.

List of Tools

• Analysis Engine: Alternates between actor and critic perspectives to generate comprehensive evaluations (based on required parameters such as content, role, nextRoundNeeded, thoughtNumber, totalThoughts).

Use Cases of this MCP Server

• Evaluating Artistic Performances: Provides dual-perspective assessments for creative works, blending creator intentions with critical feedback to enhance artistic output.
• Gap Analysis: Identifies discrepancies between intention and execution, helping developers or creators refine their processes.
• Constructive Feedback: Offers actionable improvement suggestions that balance creative vision with technical requirements.
• Complex Scenario Review: Effectively reviews scenarios needing both empathy (actor) and objectivity (critic), such as product development or user experience testing.
• Performance Reviews: Supports self-reflection and external evaluation, useful in team or individual performance assessments.

How to set it up

Windsurf

1. Ensure Node.js and npm are installed on your system.
2. Open your Windsurf configuration file.
3. Add the Actor-Critic Thinking MCP Server under the mcpServers section:

   {
     "mcpServers": {
       "actor-critic-thinking": {
         "command": "npx",
         "args": ["-y", "mcp-server-actor-critic-thinking"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running by checking the Windsurf UI or logs.

Claude

1. Install Node.js and npm if not already present.
2. Locate Claude’s configuration file.
3. Insert the following under the mcpServers key:

   {
     "mcpServers": {
       "actor-critic-thinking": {
         "command": "npx",
         "args": ["-y", "mcp-server-actor-critic-thinking"]
       }
     }
   }
   

4. Save the file and restart Claude.
5. Confirm the server is active via the platform dashboard.

Cursor

1. Install prerequisites (Node.js and npm).
2. Edit Cursor’s configuration file.
3. Add the Actor-Critic Thinking MCP Server configuration:

   {
     "mcpServers": {
       "actor-critic-thinking": {
         "command": "npx",
         "args": ["-y", "mcp-server-actor-critic-thinking"]
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Check the server status in Cursor’s UI.

Cline

1. Make sure Node.js and npm are installed.
2. Open Cline’s configuration file.
3. Add the following MCP server configuration:

   {
     "mcpServers": {
       "actor-critic-thinking": {
         "command": "npx",
         "args": ["-y", "mcp-server-actor-critic-thinking"]
       }
     }
   }
   

4. Save and restart Cline.
5. Validate the setup via Cline’s interface or logs.

Securing API Keys

• Store sensitive API keys in environment variables.
• Reference them in your configuration like this:

  {
    "mcpServers": {
      "actor-critic-thinking": {
        "command": "npx",
        "args": ["-y", "mcp-server-actor-critic-thinking"],
        "env": {
          "API_KEY": "${API_KEY}"
        },
        "inputs": {
          "apiKey": "${API_KEY}"
        }
      }
    }
  }
  

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "actor-critic-thinking": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “actor-critic-thinking” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the tables, this MCP server provides solid documentation, clear prompts, and setup instructions. However, information on sampling and roots is not present, and the toolset is relatively focused. The repository is functional and well-structured, but the scope is specialized. Overall, I would rate this MCP server a 7/10 for usability, clarity, and directness, though broader extensibility is not visible from the repo.

MCP Score