Relay
Click on "Deploy 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., "@RelayPause and ask me for confirmation before running the deployment script."
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.
Relay
The missing human checkpoint for AI coding agents.
Give your AI agent a "pause & ask" button — review, correct, or enrich every step before it runs.
Author: andeya · andeyalee@outlook.com
Why Relay?
AI coding agents are powerful — but blindly autonomous agents are risky and wasteful. Without a checkpoint, agents go on tangents, make mistakes that cascade into multiple correction rounds, and burn through your precious plan quota on wasted requests.
Relay adds a human-in-the-loop (HITL) checkpoint to any MCP-capable agent. The agent calls one tool — relay_interactive_feedback — and blocks until you submit your Answer (text, images, files). The result returns on the same JSON-RPC round trip. You catch issues early, guide accurately, and make every request count — no cloud dashboards, no extra SaaS, just a native desktop window beside your IDE.
Key advantages
Works with any MCP IDE | First-class support for Cursor, Claude Code, Windsurf, and a generic mode for others. |
100% local | All data stays on your machine — loopback HTTP only, zero telemetry, no phone-home. |
One resident GUI | A single persistent window (not a popup per request) with multi-tab session management. |
No ARG_MAX limits |
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Session continuity |
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Rich feedback | Text, screenshots, file attachments — everything the agent needs in one round trip. |
Save your plan quota | Catch mistakes early and guide precisely — no more wasted correction rounds burning through premium requests. |
Related MCP server: PulseAgent
Multi-IDE support
Launch Relay and pick your IDE. Each mode unlocks IDE-specific features — one-click MCP injection, tailored rule prompts, and (for Cursor) real-time usage monitoring.
IDE | MCP injection | Rule prompts | Usage monitoring |
Cursor | ✅ | ✅ | ✅ |
Claude Code | ✅ | ✅ | — |
Windsurf | ✅ | — | — |
Other | manual | — | — |
Quick start
1. Install — Grab the latest release (macOS, Linux, Windows) or build from source.
macOS — Gatekeeper / quarantine: CI-built .app bundles are not Apple-notarized (that requires a paid Developer ID certificate). Downloads from the browser get the com.apple.quarantine attribute, which can trigger “can’t be opened” or “damaged” warnings. Without a paid cert there is no fully automatic fix for all users; options are:
Recommended: In Finder, Control-click (or right-click) the app → Open and confirm once (stores an exception for that app).
CLI (one-time): clear quarantine after copying the app to
Applications(adjust the path if yours differs):
xattr -dr com.apple.quarantine "/Applications/Relay.app"2. Launch & choose IDE — Run relay and click your IDE card, or go directly:
relay gui-cursor # Cursor mode
relay gui-claudecode # Claude Code mode
relay gui-windsurf # Windsurf mode3. Wire MCP — Point your IDE at the Relay binary. Example for Cursor:
{
"mcpServers": {
"relay-mcp": {
"command": "/path/to/relay",
"args": ["mcp-cursor"],
"autoApprove": ["relay_interactive_feedback"]
}
}
}Cursor uses
.cursor/mcp.json(per-repo, merged with~/.cursor/mcp.json). For WSL agent + Windowsrelay.exe, add--exe_in_wsl:["mcp-cursor", "--exe_in_wsl"]. See docs/HTTP_IPC.md for details.
Or use Settings → Environment & MCP inside Relay for one-click setup and to copy the MCP JSON directly:
4. Install rule prompts — Go to Settings → Rule prompts and install with one click. This teaches the agent to call relay_interactive_feedback every turn and maintain relay_mcp_session_id.
Cursor rule file — One-click install writes relay-interactive-feedback.mdc under user ~/.cursor/rules/ (Windows: %USERPROFILE%\.cursor\rules\). That path is separate from a repo’s .cursor/rules/; copy or symlink there if you only use project rules.
Agent still skips relay_interactive_feedback? Ensure MCP server relay-mcp is enabled; approve the tool when prompted (or set "autoApprove": ["relay_interactive_feedback"]); reload Cursor after editing the rule file on disk. Rule files guide the model — they are not hard guarantees.
Relay human-in-the-loop (end-to-end)
After Quick start steps 2–4, each turn follows this path (transport detail: docs/HTTP_IPC.md; vocabulary: docs/TERMINOLOGY.md):
sequenceDiagram
participant Agent
participant Mcp as relay_mcp-ide
participant Http as GUI_HTTP_127.0.0.1
participant You
Agent->>Mcp: tools/call relay_interactive_feedback (retell, session, commands/skills…)
Mcp->>Http: POST /v1/feedback → request_id
Http->>You: tab shows retell
You->>Http: Answer / dismiss / idle cutoff
Http-->>Mcp: GET …/wait → JSON result
Mcp-->>Agent: same tools/call responseMCP runs — The IDE launches
relay mcp-<cli_id>(stdio), e.g.mcp-cursor. The matching GUI isrelay gui-<cli_id>(often already open).Agent calls the tool — Non-empty
retell. New session: omitrelay_mcp_session_id(or empty) and sendcommandsandskills(each may be[]only if the host truly exposes nothing). Continue: pass therelay_mcp_session_idfrom the previous result.MCP reaches the GUI — Reads
gui_endpoint_<cli_id>.jsonin your Configuration & paths directory (e.g.gui_endpoint_cursor.json), or spawnsrelay gui-<cli_id>and waits ≤ ~45 s. ThenPOST /v1/feedbackand blocks onGET /v1/feedback/wait/:iduntil the tab completes.You interact — Submit Answer, attach files, dismiss, or let the ~60 min idle cleanup return empty
human(same as dismiss from the agent’s perspective).Same JSON-RPC returns — Body includes
relay_mcp_session_id,human,cmd_skill_count, optionalattachments. The next turn must send thatrelay_mcp_session_idunless starting a new tab.
Rules vs MCP: Step 4’s rule prompt install writes relay-interactive-feedback.mdc under user ~/.cursor/rules/ (Cursor only). Rules encourage the loop; relay-mcp in MCP settings executes it.
Architecture
flowchart LR
IDE[IDE / Agent] -->|stdio JSON-RPC| MCP["relay mcp-{ide}"]
MCP -->|read or spawn| GUI["relay gui-{ide}"]
MCP <-->|127.0.0.1 Bearer| HTTP[Tauri HTTP API]
HTTP <--> UI[Vue tabs]
UI --- User((You))
MCP -->|JSON result| IDErelay mcp-{ide}— Stdio MCP server (clap). Handlesinitialize,tools/list,tools/call. Concurrent human rounds on one connection. Optional auto-reply rules.relay/relay gui-<cli_id>— Tauri app + HTTP on127.0.0.1:0. Writesgui_endpoint_<cli_id>.json(e.g.gui_endpoint_cursor.json) with{ port, token, pid }; cleans up on exit.Bridge — MCP reads the endpoint file; if missing, spawns
gui-{ide}and polls up to ~45 s. ThenPOST /v1/feedback→GET /v1/feedback/wait/:id. The wait resolves on submit, dismiss, supersede, or ~60 min idle.
MCP tool: relay_interactive_feedback
Argument | Required | Meaning |
| yes (non-empty) | This turn's user-visible assistant reply, verbatim. |
| if you have one | Continue the same session; returned in the JSON result. |
| new tab: required | Array of IDE commands for slash-completion. |
| same as commands | Array of IDE skills. Same merge/dedupe rules. |
Pause MCP (Settings): sentinel <<<RELAY_MCP_PAUSED>>> — do not call again until resumed.
Features at a glance
Multi-tab hub — Each request opens or refreshes a tab.
relay_mcp_session_idmerges streams. Labels show MM-DD HH:mm:ss with turn-status color indicators.Rich composer — Enter to submit, Shift+Enter for newline, ⌘/Ctrl+Enter to submit & close. Paste images, attach files — they appear as
attachmentsin the tool result.Cursor Usage monitoring — Auto-detect your Cursor token (cross-platform decryption), view plan quotas, request history, and predicted quota exhaustion in a live popover.
Auto-reply —
auto_reply_oneshot.txt/auto_reply_loop.txtfor instant0|replyresponses without opening the UI.Local storage —
feedback_log.txt,qa_archive/<session_id>.jsonl, configurable attachment retention (default 30 days).CLI —
relay feedback --retell "…"prints JSON on stdout;--timeoutfor CI/automation.
CLI reference
Command | Role |
| Open IDE selection page |
| Launch GUI in Cursor mode |
| Launch GUI in Claude Code mode |
| Launch GUI in Windsurf mode |
| MCP stdio server for Cursor (what the IDE runs) |
| MCP stdio server for Claude Code |
| MCP stdio server for Windsurf |
| Terminal tryout; |
Only one GUI process per IDE mode is allowed; bare relay (no mode) can run multiple instances.
Configuration & paths
Data lives under your OS application-data directory (directories::ProjectDirs → config_dir()):
OS | Path |
macOS |
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Linux |
|
Windows |
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Key files: feedback_log.txt, qa_archive/*.jsonl, ui_locale.json, gui_endpoint_<cli_id>.json (e.g. gui_endpoint_cursor.json), relay_gui_<cli_id>_alive.marker, mcp_pause.json, attachment_retention.json, auto_reply_*.txt, legacy gui_endpoint.json when applicable.
Build
npm install
npm run build # Vite frontend
cargo build --manifest-path src-tauri/Cargo.toml --release
npm run tauri build # installers / .app / etc.Develop:
npm run lint && npm run typecheck
npm run tauri:devIcons (from src-tauri/icons/source/relay-icon.svg):
npm run icons:buildCI: lint, typecheck, Vite, cargo fmt, clippy -D warnings, cargo test — see docs/RELEASING.md.
Documentation
Doc | Content |
HTTP API, timeouts, WSL path rewrite | |
Session ID & tab labels | |
Vocabulary + binaries / endpoint files | |
Releases & CI |
Privacy
Data stays on device. All answers, logs, attachments, and settings are written only under your OS user paths. The GUI and MCP process communicate over 127.0.0.1 — nothing leaves your machine.
No telemetry. Relay ships no analytics SDKs, crash reporters, or remote instrumentation. Local files like feedback_log.txt may contain sensitive content — handle them accordingly.
Acknowledgements
Inspired by interactive-feedback-mcp. Relay replaces per-request subprocess UIs with a resident GUI and a Bearer-authenticated local HTTP layer.
License
This server cannot be deployed
Maintenance
Related MCP Connectors
Preventive human-approval write-gate for AI agents: writes commit only after a human approves.
Human-in-the-loop for AI agents over MCP: durable approvals with a hosted review page & audit trail
Human-in-the-loop for AI coding agents — ask questions, get approvals via Slack.
MCP enforcement layer that intercepts AI agent actions and blocks rule violations before execution.
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