Let your local agents trade on internal prediction markets
AI agents trading on internal prediction markets to surface hidden team knowledge.
The CoChat MCP to share and collaborate on plans in claude, opencode, cursor etc
MCP bridge for team code review on agent plans, but execution maturity unclear.
Software engineers using AI coding agents (Claude Code, Cursor, OpenCode); teams that want multi-agent code review
Cursor · Continue · GitHub Copilot Chat
The problem: When Claude Code creates an implementation plan, it lives in your terminal session. Nobody else sees it until it becomes a PR. If you want GPT to check the architecture or a teammate to flag issues, you're copy-pasting between windows.
This MCP server fixes that. When your agent creates a plan, it gets shared as a collaborative thread in CoChat. Engineers comment on it, other AI models review it, and you pull all the feedback back into your agent's context with one command. Decisions can be saved as project memories that persist across sessions and are searchable by anyone.
What it does:
Plans: Auto-shared as collaborative threads. Pull feedback back into your terminal. Cross-model review: Have GPT review your Claude plan, or vice versa. Project memories: Semantic memory that persists across sessions, models, and people. Ask: Query your project's knowledge base from the terminal. Auto-scoping: Detects your project from git remote. No config needed. Setup is one command per agent. Auto-share behavior is configurable (off/plan/all).
MIT licensed, available on npm: npx @cochatai/mcp-cochat
Happy to answer questions about the architecture or the MCP protocol integration.
AI agents trading on internal prediction markets to surface hidden team knowledge.
Zero glue code: just prompt an agent connected to two MCP servers.
Markdown-first review gates for AI agents, but the problem (agent hallucination in code) is still nascent.
Walkthrough mode turns code review into pair programming where the agent points at your screen.
MCP-integrated review gates for AI agents—annotation-driven handoffs save context across sessions.
Human-in-the-loop review layer for Claude Code before irreversible agent actions.