Twosplit MCP Server
The Twosplit MCP Server leverages multiple Claude AI instances to generate optimized responses:
Sends the same prompt to two separate instances of a specified Claude model
Uses a third instance to analyze and either select the best response or combine elements from both
Returns the final optimized response along with the original responses and source attribution
Supports multiple Claude models including claude-3-opus-latest, claude-3-5-sonnet-latest, claude-3-5-haiku-latest, and claude-3-haiku-20240307
Requires Anthropic API key configuration
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Twosplit MCP Server write a product description for a smart coffee mug"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Twosplit MCP Server
An MCP server that leverages multiple Claude instances to provide enhanced responses. It sends the same prompt to two separate instances of Claude and uses a third instance to combine or select the best elements from both responses.
Features
Supports multiple Claude models:
claude-3-opus-latest
claude-3-5-sonnet-latest
claude-3-5-haiku-latest
claude-3-haiku-20240307
Gets single, direct responses from each AI
Shows original responses and source attribution
Returns optimized final response
Related MCP server: prompte-mcp
Installation
Clone the repository
Install dependencies:
npm installBuild the server:
npm run buildConfiguration
The server requires an Anthropic API key to function. Set it as an environment variable:
export ANTHROPIC_API_KEY=your-api-key-hereUsage
The server provides a single tool called twosplit with the following parameters:
prompt(required): The prompt to send to Claudemodel(required): The Claude model to use (must be one of the supported models listed above)
Example tool usage in Claude:
<use_mcp_tool>
<server_name>twosplit</server_name>
<tool_name>twosplit</tool_name>
<arguments>
{
"prompt": "Write a short story about a robot learning to paint",
"model": "claude-3-5-sonnet-latest"
}
</arguments>
</use_mcp_tool>The response will include:
The final optimized response
Original responses from both AIs
Source attribution showing which parts came from which AI
How it Works
The server sends the same prompt to two separate instances of the specified Claude model, requesting a single direct response
A third instance analyzes both responses and either:
Selects the single best response if one is clearly superior
Creates a new response that combines the best elements from both responses
The final response, original responses, and source attribution are all included in the output
Development
To run the server in watch mode during development:
npm run watchTo inspect the server's capabilities:
npm run inspectorAvailable Tools
1 tooltwosplitC
Get multiple AI perspectives and combine them into the best response
Inputs: model (required), prompt (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as getting multiple AI perspectives and combining them, which is distinct and unambiguous in this single-tool context.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'twosplit' follows a single pattern with no deviations or mixing of conventions to evaluate.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. For a server named 'Twosplit MCP Server' that aims to provide multiple AI perspectives, one tool feels thin and under-scoped, potentially leaving gaps in operations like configuration or result refinement.
The tool surface is severely incomplete for the implied domain of handling AI perspectives. While the 'twosplit' tool covers the core action, there are obvious gaps such as no tools for managing perspectives, adjusting combination parameters, or retrieving historical results, which could lead to agent failures in more complex workflows.
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