E

Ephor Collaboration

An MCP server that supports collaborative debates among multiple AI agents to achieve consensus - based decision - making
2 points
9

What is the LLM Responses MCP Server?

The LLM Responses MCP Server is a server based on the Model Context Protocol (MCP) that allows multiple AI assistants (such as large language models) to engage in collaborative discussions around user questions and ultimately reach a consensus.

How to use the LLM Responses MCP Server?

Users can register the initial responses of participants through simple steps and then submit subsequent replies to gradually reach a consensus.

Applicable scenarios

Suitable for complex problems that require multi - party discussions and decision - making, such as legal consultations, academic research, or creative writing.

Main features

Participant registrationSupports AI assistants to join the discussion by providing initial responses.
Real - time debateAllows AI assistants to submit subsequent replies during the discussion process.
Reach a consensusAchieve a final consensus through multi - round interactions.

Advantages and limitations

Advantages
Supports multi - round conversations and real - time feedback.
Promotes in - depth exchanges between different viewpoints.
Suitable for collaborative solutions to complex problems.
Limitations
It may take a long time to reach a consensus.
Has certain requirements for network latency.

How to use

Install dependencies
Run the command `bun install` to install the dependencies required for the project.
Build the code
Compile the TypeScript code to generate an executable file.
Start the server
Start the server in development mode.

Usage examples

Case 1: Philosophical question discussionMultiple AI assistants discuss the topic of 'the meaning of life'.
Case 2: Legal question consultationSimulate a lawyer team negotiating the details of a case.

Frequently Asked Questions

How to start a new discussion session?
Does the server support real - time updates?
How long can a discussion session last?

Related resources

Official documentation
Detailed user manual and API reference.
GitHub repository
Source code and contribution guidelines.
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