coreason_multi_agent_debate
OfficialClick on "Install 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., "@coreason_multi_agent_debatedebate the ethical implications of autonomous vehicles"
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.
coreason_multi_agent_debate
Dynamic multi agent debate MCP server
Getting Started
Prerequisites
Python 3.14+
uv
Installation
Clone the repository:
git clone https://github.com/CoReason-AI/coreason_multi_agent_debate.git cd coreason_multi_agent_debateInstall dependencies:
uv sync --all-extras --dev
Usage
Run the linter:
uv run pre-commit run --all-filesRun the tests:
uv run pytest
Available Tools
1 toolmultiagentdebateA
Structured multi‑persona debate tool.
Call sequence (typical):
Each persona registers once with action:"register".
Personas alternate action:"argue" (fresh point) or "rebut" (counter a targetAgentId).
A special persona (or either side) issues action:"judge" with a verdict text (first line should be "pro", "con", or "inconclusive").
Set needsMoreRounds:false only when the debate is finished and a verdict stands.
Parameters:
agentId (string) : "pro", "con", "judge", or any custom ID
round (int ≥1) : Debate round number
action (string) : "register" | "argue" | "rebut" | "judge"
content (string, optional) : Argument text or verdict
targetAgentId (string opt.) : Agent being rebutted (only for action:"rebut")
needsMoreRounds (boolean) : True if additional debate rounds desired
| Name | Required | Description | Default |
|---|---|---|---|
| round | Yes | ||
| action | Yes | ||
| agentId | Yes | ||
| content | No | ||
| targetAgentId | No | ||
| needsMoreRounds | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses the expected behavior: each persona registers once, alternates between argue/rebut, and a judge issues a verdict. It also clarifies the verdict format and termination condition, ensuring transparency about the tool's protocol.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a numbered list and bullet points, making it easy to follow. Every sentence adds value without redundancy, achieving high conciseness while covering all necessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the protocol, parameter roles, and termination condition comprehensively. However, it does not mention what the tool returns or any output format, which could be helpful for an agent to interpret results. This minor gap prevents a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, explaining each parameter's purpose, constraints (e.g., round >=1, action enum), and context (e.g., targetAgentId only for rebut). It also describes the special formatting for judge verdicts, compensating for the 0% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'Structured multi‑persona debate tool' and explains the primary purpose and flow of the tool. It specifies the actions (register, argue, rebut, judge) and their sequence, making the purpose distinct and comprehensible.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a detailed call sequence with steps for each persona, including when to register, argue, rebut, and judge. It explains the verdict format and when to set needsMoreRounds to false, offering clear guidance on proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.7- First observed
multiagentdebate
TDQS
Only one tool exists, so there is no possibility of confusion between tools. The tool's purpose is clearly defined.
The single tool name 'multiagentdebate' is descriptive and uses consistent camelCase. There are no naming conflicts to evaluate.
With just one tool, the server is minimal. While the tool is comprehensive for debates, a single tool feels slightly thin for typical MCP servers, but it is borderline acceptable for a niche function.
The tool covers the full debate lifecycle: registration, argument, rebuttal, and judging. All necessary actions are included, with no obvious gaps for the intended domain.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server for building and testing AI agents with multi-model experimentation and insights.
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
NeuralBrain MCP Server - RAG, Vector Memory, LLM Routing, Agent Identity, x402 Payments
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables collaborative debates between multiple AI agents, allowing them to discuss and reach consensus on user prompts.1MIT
- FlicenseNot gradedqualityDmaintenanceAn MCP server that provides formal reasoning and argument validation tools for AI agents based on established computational argumentation theories. It enables structured argument analysis, defeasible reasoning, and dialogue management using frameworks like Dung, Toulmin, and Walton's schemes.-
- AlicenseNot gradedqualityBmaintenanceAn MCP server that enables multi-model debate and consensus building through a single tool. It orchestrates multiple AI models from various providers to debate topics and reach validated conclusions with real-time progress tracking.203MIT
- AlicenseNot gradedqualityCmaintenanceAn MCP server that enables multi-round AI expert debates with dynamic expert generation, cross-debate, and Tufte-style infographic export.6MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/CoReason-AI/coreason-multi-agent-debate'
If you have feedback or need assistance with the MCP directory API, please join our Discord server