Multi-agent orchestration for Claude Code — consensus review, adaptive dispatch, skill learning
{
"mcpServers": {
"io-github-ataberk-xyz-gossipcat": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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Multi-agent orchestration for Claude Code — consensus review, adaptive dispatch, skill learning
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
Is it maintained?
Commit history unknown.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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agentic orchestration framework — agents that learn, adapt, and get better every round.
Install · First Run · Daily Use · Dashboard · Troubleshooting · Config · For AI Agents
Gossipcat is an MCP server that orchestrates multiple AI agents to review your code in parallel. Agents independently review, then cross-review each other's findings. Agreements are confirmed. Hallucinations are caught and penalized. Over time, each agent builds an accuracy profile — the system learns who to trust for what.
| Without gossipcat | With gossipcat |
|---|---|
| One AI reviews your code — and hallucinates a finding you waste 20 minutes on | Multiple agents cross-check each other — hallucinations get caught before you see them |
| Every agent gets the same tasks regardless of track record | Dispatch weights route tasks to the agent with the best accuracy in that category |
| An agent keeps making the same class of mistake | Skill files are auto-generated from failure data and injected into future prompts |
| You don't know which agent to trust | Accuracy, uniqueness, and reliability scores are tracked per agent, per category |
Consensus Review3+ agents review independently, then cross-review each other. Findings tagged as CONFIRMED, DISPUTED, or UNIQUE. |
Adaptive DispatchAgent accuracy is tracked per-category. Dispatch weights adjust automatically — the best agent for the job gets picked. |
Skill DevelopmentWhen an agent keeps failing in a category, targeted skills are generated from failure data and injected into future prompts. Effectiveness is measured with a z-test on post-bind signals — passed, failed, or inconclusive. |
Multi-ProviderMix Anthropic, Google, OpenAI, and OpenClaw agents in one team. Each brings different strengths. Native agents need no API key. 🦞 Lobster friendly. |
Live DashboardReal-time view of tasks, consensus reports, agent scores, and activity feed. Terminal Amber theme. WebSocket updates. |