MCP server: One MCP server.

Gossipcat

@ataberk-xyzMCP server · External

Multi-agent orchestration for Claude Code — consensus review, adaptive dispatch, skill learning

Code reviewv0.2.1MITTypeScript
Freeno charge per call

Runs on your machine from its package; the steps are below.

Gossipcat
From the README · 1 of 2

What it does

  1. Reads
  2. Writes
  3. High impact

At most: moves money, runs code or commands, deletes data, or sends messages for you.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

npmgossipcatv0.2.1

Run

npx -y gossipcat

MCP client config

{
  "mcpServers": {
    "gossipcat": {
      "command": "npx",
      "args": [
        "-y",
        "gossipcat"
      ]
    }
  }
}
README5 min read

Gossipcat

Multi-agent consensus code review.
AI reviewers lie confidently. Gossipcat makes them check each other — against your actual code.

TypeScript · MCP · Claude Code · Cursor · multi-agent

npm tests MIT stars

Quick start · How it works · Guide · Handbook · Changelog


A single AI reviewer will, with total confidence, report bugs that aren't there. You read the finding, you go look, you waste twenty minutes — the code was fine. No second opinion, no track record, no way to tell a real catch from a hallucination until you've paid for it.

Gossipcat runs several agents in parallel, has each one verify its peers' findings against your real file:line, and only surfaces what survives. When an agent invents a finding, a peer catches it and the agent's accuracy score drops — over time the system routes each kind of work to whoever is measurably reliable at it. The verdict comes from citation checks against your source, never from one model grading another.

It runs as an MCP server inside Claude Code and Cursor, with a live operator dashboard and a two-way browser chat bridge into the running orchestrator.

Gossipcat dashboard — live fleet view with per-agent accuracy rings, signal volume, and recent hallucination catches


Reading a report

Your whole job is four tags:

TagMeansWhat you do
CONFIRMEDMultiple agents found it and verified it against the codeFix it
UNIQUEOne agent found it, cross-checked and held upFix it — high signal
DISPUTEDAgents disagreed; gossipcat re-checked the codeTrust the verdict
UNVERIFIEDLooks real but wasn't cross-checked yetGlance, then verify

The DISPUTED false alarm that cross-review kills is the bug a solo reviewer would have shipped to you. That delta is the whole point.


How it works

mermaid
flowchart LR
    A([agent review]) -->|cites file:line| B([peer cross-review])
    B -->|verifies against code| C{verdict}
    C -->|confirmed| D[reward signal]
    C -->|hallucination| E[penalty signal]
    D --> F[competency score]
    E --> F
    F -->|steer dispatch| G([next agent pick])
    E -->|≥3 in category| H[auto-generate skill]
    H -->|inject into prompt| A
    G --> A
    style A fill:#0ea5e9,stroke:#0369a1,color:#fff
    style H fill:#f59e0b,stroke:#b45309,color:#fff
    style D fill:#10b981,stroke:#047857,color:#fff
    style E fill:#ef4444,stroke:#b91c1c,color:#fff

Every finding must cite a real file:line. Peers verify the citation mechanically — agree, disagree, or new — and the verified outcomes become reward signals that update per-agent competency scores. An agent that keeps failing in one category gets a skill file auto-generated from its own failure history and injected into future prompts; skills that don't measurably help are statistically demoted. It's in-context reinforcement learning at the prompt layer: the reward is grounded in your source code, the "policy update" is a markdown file, and no weights are ever touched.

Since v0.8, skills also activate by task relevance instead of shipping wholesale, and agents can pull skills on demand mid-task — including your own Claude Code project skills from .claude/skills/, no duplication needed.


Quick start

Node 22+, and either Claude Code or Cursor.

bash
npx skills add gossipcat-ai/gossipcat-ai   # fastest — installer skill walks you through it

or manually:

bash
npm install -g gossipcat
claude mcp add gossipcat -s user -- gossipcat     # Claude Code
# Cursor: add { "gossipcat": { "command": "gossipcat" } } to .cursor/mcp.json

Then, in any project:

"Set up a gossipcat team for this project." "Do a consensus review of my recent changes."

The smallest working team — sonnet-reviewer + haiku-researcher — is fully native and needs zero API keys: it runs on your existing Claude Code / Cursor subscription. Relay agents (Gemini, OpenAI, Grok, DeepSeek, Ollama, any OpenAI-compatible endpoint) are optional and mix freely.

First run, daily recipes, dashboard, configuration, and troubleshooting: docs/GUIDE.md.


Compared to the alternatives

Filters hallucinationsImproves over time
Gossipcat — 3+ agents cross-review; confirmed bugs onlyYes — peers catch and penalize hallucinations mechanicallyYes — accuracy steers dispatch; skill files fix repeat failures
Single-agent review (IDE built-in)No — hallucinations ship as findingsNo feedback loop
Model-grades-model reviewPartial — the judge hallucinates tooScores aren't wired to dispatch
Lint-style PR botsNoNo

The difference is ground truth: findings are verified against actual file:line citations in your codebase, which is what makes the reward signal trustworthy enough to automate.


Architecture

gossipcat/
  apps/cli/               MCP server, host-aware native agent bridge, boot sequence
  packages/
    orchestrator/         Dispatch pipeline, consensus engine, memory, skills, scoring
    relay/                WebSocket relay server, dashboard REST/WS API
    dashboard-v2/         React + Vite + shadcn/ui frontend (see DESIGN.md)
    client/               WebSocket client for relay connections
    tools/                File / shell / git tools for worker agents
    types/                Shared types and message protocol

Native agents run as host subagents (Claude Code Agent() / Cursor Task()) on your subscription — no API key. Relay agents run as WebSocket workers against any provider. Both participate equally in consensus, memory, and skill development.

Reading this as a Claude Code or Cursor instance? Call gossip_status() — it boots your full operating rules. The internals and design invariants live in docs/HANDBOOK.md.


Docs

docs/GUIDE.mdOperator guide — first run, daily recipes, dashboard, config, tools, troubleshooting
docs/HANDBOOK.mdInternals — architectural invariants, the signal pipeline, why the design is shaped this way
CHANGELOG.mdReleases, with per-version upgrade steps
CLAUDE.mdThe operating rules gossipcat's own agents follow while developing gossipcat

Roadmap

Dashboard enrichment (graphs, trends, session history) · local Postgres migration · Windsurf / VS Code native parity · standalone CLI. Shipped work: releases.

Contributing

Bug reports, ideas, and PRs welcome — open an issue or ask in-session "file a gossipcat bug report about …". Fork, branch, npm test, conventional commits; details in CONTRIBUTING.md.

License

MIT

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