AgenticMarketRoundtable MCP
ECOSYSTEM REFERENCENO AUTHOtherOpen SourceSource: @agenticmarket· community reference
Multi-model AI council MCP server that enables collaborative reasoning for architecture, debugging, code review, and engineering decisions.
Setup Guide
{
"mcpServers": {
"roundtable-mcp": {
"command": "uvx",
"args": [
"roundtable-ai@latest"
],
"env": {
"CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
}
}
}
}Tools
list-models
List all available AI models grouped by thinking levels (low, medium, high), including default selections, credit costs, and capabilities. Use this before running consultations to choose the optimal model configuration.
list-sessions
Retrieve a list of previous MCP sessions with metadata such as prompt, tool used, quality score, and credits consumed. Useful for reviewing past AI council interactions and performance.
get-session
Fetch complete details of a specific MCP session using its session ID, including full discussion, participant responses, and final synthesized output.
get-logs
Query structured logs from MCP executions with filters like session ID, severity level, event type, and time range. Ideal for debugging, monitoring, and auditing tool activity.
check-usage
Check current usage statistics including remaining credits, consumption history, and plan limits to manage resource allocation effectively.
get-thread-link
Retrieve the dashboard and public URL for a specific discussion thread, enabling sharing and access to previous AI council sessions.
set-thread-visibility
Update the visibility of a discussion thread to public or private using its session ID, allowing controlled sharing of AI-generated outputs.
consult-council
Consult a multi-model AI council where different models discuss an engineering problem sequentially and a moderator synthesizes the final answer. Supports auto-mode (AI selects models) and manual configuration.
design-architecture
Run an architecture design council where roles like Systems Architect, Infrastructure Engineer, and DX Advocate evaluate system design decisions and generate structured Architecture Decision Records (ADR).
review-code
Perform a multi-perspective code review using roles such as Senior Engineer, Security Reviewer, and Performance Analyst, followed by a synthesized report with actionable insights.
plan-implementation
Generate a detailed implementation plan for a feature using an AI council including Tech Lead, Senior Engineer, and QA Strategist, covering steps, risks, and acceptance criteria.
debug-issue
Diagnose bugs using a debugging council composed of Root Cause Analyst, Systems Engineer, and Edge Case Investigator, providing root cause analysis and fix recommendations.
assess-tradeoffs
Evaluate trade-offs between different technical approaches using perspectives like Pragmatist, Skeptic, and Futurist, delivering balanced pros and cons for decision-making.
Compatibility
About
AI Council MCP
AI Council MCP is a multi-agent reasoning server built on the Model Context Protocol (MCP) that enables AI models to collaborate, debate, and synthesize decisions across engineering workflows.
Instead of relying on a single model, it orchestrates multiple AI roles (e.g., architect, reviewer, debugger) to produce higher-quality, bias-reduced, and structured outputs.
🧠 Core Concept
AI Council MCP implements a multi-model consensus system:
- Multiple AI models or roles analyze the same problem
- Each contributes a perspective (sequential or parallel)
- A moderator synthesizes the final answer
This approach improves reliability, reduces hallucination, and enables deeper reasoning compared to single-model outputs. :contentReference[oaicite:1]{index=1}
⚡ Key Features
-
🧑⚖️ Multi-Agent Reasoning Combine perspectives from multiple AI roles for better decisions
-
🧠 Consensus-Based Outputs Final answers are synthesized from multiple viewpoints
-
⚙️ Configurable Models & Roles Choose models, thinking levels, and interaction modes
-
🔍 Full Session Observability Access logs, sessions, and execution metadata
-
💳 Usage & Credit Tracking Monitor consumption and plan limits
-
🔗 Shareable Threads Make discussions public and share via links
🛠️ Exposed Tools (13)
📊 System & Meta
-
list-models
List available AI models grouped by thinking level, including costs and capabilities -
check-usage
Retrieve credit usage, limits, and plan details
🧾 Sessions & Logs
-
list-sessions
View past MCP sessions with metadata and performance metrics -
get-session
Retrieve full details of a specific session including all responses -
get-logs
Query structured logs with filters (session, severity, time range)
🔗 Threads & Sharing
-
get-thread-link
Get dashboard and public URL for a discussion thread -
set-thread-visibility
Set a thread as public or private
🧠 Core AI Council Tools
-
consult-council
Run a multi-model discussion where AI agents debate and a moderator synthesizes -
design-architecture
Architecture decision council (outputs ADR format) -
review-code
Multi-role code review (security, performance, engineering) -
plan-implementation
Generate structured implementation plans with risks and acceptance criteria -
debug-issue
Diagnose issues using root-cause and systems analysis -
assess-tradeoffs
Evaluate options using multiple perspectives (pragmatist, skeptic, futurist)
🎯 When to Use
Use AI Council MCP when:
- You need high-confidence engineering decisions
- Building AI-powered developer tools or copilots
- Performing architecture design or system planning
- Running multi-perspective code reviews
- Debugging complex issues with structured reasoning
- Evaluating trade-offs between technologies or approaches
❌ When NOT to Use
- Simple queries (overkill vs single LLM)
- Real-time low-latency tasks
- Non-engineering or non-technical use cases
🧑💻 Ideal Use Cases
- AI coding assistants with deep reasoning
- Architecture review systems
- Automated code review pipelines
- Engineering decision support tools
- Agentic workflows and orchestration systems
🏗️ Architecture Insight
This MCP server follows the standard MCP model:
- MCP exposes tools via a structured interface
- AI clients call tools using standardized protocol
- Multiple models interact through orchestrated workflows
This enables modular, scalable AI systems with reusable capabilities. :contentReference[oaicite:2]{index=2}
🏷️ Categories
dev-toolkitai-agentsengineeringdecision-engine
🏷️ Tags
mcp multi-agent ai-council code-review architecture debugging agentic-ai
🏅 Badge
Community
(Use this under AgenticMarket community badge category)
⚠️ Notes
- Requires credits for multi-model execution
- Output quality depends on model selection and configuration
- Best suited for complex reasoning workflows
🔥 Positioning
AI Council MCP is not just a toolset — it is a decision intelligence layer for engineering workflows.
It transforms AI from:
- ❌ Single-response systems
→ into - ✅ Multi-agent reasoning systems
Install and Troubleshooting Intent Coverage
Developer-install and troubleshooting intent for community MCP server listings.
install mcp server / mcp server setup guide
mcp json config example / vscode mcp setup
mcp server not working / mcp tools not showing
mcp server compatibility matrix / cursor vs vscode mcp compatibility
mcp server monetization options / convert community mcp server to paid listing
Related Setup, Debug, and Learning Links
CLI installation guide
Install baseline for all IDEs before listing-specific setup.
Using servers guide
Covers runtime usage patterns and auth flow.
Cursor setup walkthrough
High-intent setup path for developer troubleshooting journeys.
Troubleshooting: server not working
Common failure modes for install and runtime issues.
Troubleshooting: tools not showing
Covers discovery/listing failures across major IDEs.
Related explore entry: Memory
Keeps same-intent users on matched category and tool shape.
Related explore entry: Sequential Thinking
Keeps same-intent users on matched category and tool shape.
Related explore entry: Everything MCP Server
Keeps same-intent users on matched category and tool shape.
Related explore entry: Git MCP Server
Keeps same-intent users on matched category and tool shape.
Install this server instantly with the AgenticMarket CLI — zero config, auto-detects your IDE.
$npx agenticmarket install roundtable-mcpMemory
Give your AI assistant persistent memory across conversations. The Memory server stores entities, relations, and observations in a local knowledge graph that persists between sessions.
Sequential Thinking
Enhance your AI assistant's reasoning with structured, step-by-step thinking. Supports revisions, branching, and dynamic adjustment of reasoning depth.
Everything MCP Server
The official MCP reference server that exercises every protocol feature — prompts, tools, resources, sampling, and all transports. Built for MCP client developers and testing.
Git MCP Server
Let your AI assistant interact with Git repositories directly. Status, diff, commit, branch, and log — all accessible to your LLM through 12 Git tools.