zulipmcp

Run AI agents in Zulip as @mentionable bots — or wire into any MCP client. Also works as a Python library.

Quickstart

1. Install the package:

   hljs language-bash

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   uv add zulipmcp --git https://github.com/windborne/zulipmcp.git
   

2. Add a .zuliprc file to your project root with your Zulip bot credentials. See Add a bot or integration for instructions on making a bot. The bot type must be "generic."

3. Add the MCP server to your .mcp.json:

   hljs language-json

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   {
     "mcpServers": {
       "zulip": {
         "command": "uv",
         "args": ["run", "python", "-m", "zulipmcp.mcp"]
       }
     }
   }
   

4. Restart your MCP client. The Zulip tools should now be available.

Requirements

• Python >=3.10, managed with uv
• A .zuliprc file for Zulip API auth (see Quickstart)
• For listener mode: the selected backend CLI installed and authenticated (claude by default, codex with --backend codex, or opencode with --backend opencode)

Entry Points

Library Usage

zulipmcp can also be imported directly as a Python library:

import zulipmcp

# Fetch and format recent messages
messages = zulipmcp.get_messages(hours_back=24, channels=["engineering"])
print(zulipmcp.format_messages(messages))

# Send a message
zulipmcp.send_message("engineering", "general", "Hello from Python!")

# Configure MCP hooks before starting the server
zulipmcp.configure(
    message_prefix=lambda: "[bot] ",
    on_session_end=lambda session: print(f"Session ended in #{session.stream}"),
)

Listener

The optional zulipmcp.listener module watches Zulip for @mentions and spawns one headless agent session per (stream, topic). It supports Claude Code by default, Codex with --backend codex, and OpenCode with --backend opencode. It's the glue between Zulip events and the agent backend -- the MCP server handles all the Zulip tools, the listener just handles lifecycle.

# Minimal -- uses ./.zuliprc, ./.mcp.json (if present), and the bundled default prompt
uv run python -m zulipmcp.listener

# Full -- override MCP config and system prompt
uv run python -m zulipmcp.listener \
    --mcp-config .mcp.json \
    --system-prompt agent.md \
    --log-dir ./logs

# Recommended: Claude Code with Opus 4.6
uv run python -m zulipmcp.listener -- --model claude-opus-4-6

# Recommended: Codex with GPT-5.5 and medium reasoning
uv run python -m zulipmcp.listener --backend codex -- \
    --model gpt-5.5 \
    -c 'model_reasoning_effort="medium"'

# Recommended: OpenCode with any provider (Qwen, Llama, Gemini, etc.)
uv run python -m zulipmcp.listener --backend opencode \
    --opencode-model anthropic/claude-sonnet-4-5

uv run python -m zulipmcp.listener --backend opencode \
    --opencode-model ollama/qwen3:235b

# Pass additional backend-specific flags after --
uv run python -m zulipmcp.listener -- --strict-mcp-config --effort medium
uv run python -m zulipmcp.listener --backend codex -- -c 'model_verbosity="low"'
uv run python -m zulipmcp.listener --backend opencode -- --verbose

Flags:

Each session gets TRIGGER_MESSAGE_ID and SESSION_USER_EMAIL set automatically so set_context() anchors to the @mention and hooks can identify the requester.

The listener intentionally does not set model or reasoning defaults. Backend CLIs and model aliases move over time, so use the backend's user config or pass flags after --. For reproducible production behavior, pin exact backend model IDs in your deployment config instead of relying on aliases.

Custom --system-prompt files are backend instructions, not the initial task. The listener still sends a short per-session bootstrap prompt with the target stream/topic and the Zulip lifecycle contract: initialize context, send visible text through reply(), then call listen() when yielding for follow-ups.

Codex sessions launch with web search enabled to match Claude Code's default web-fetch capability. For Codex, the .mcp.json adapter whitelists inherited environment variable names for translated stdio MCP servers, mirroring Claude-style subprocess inheritance without putting env values in argv. It also forwards Zulip's direct auto-init SESSION_STREAM/SESSION_TOPIC pair when present and sets tool_timeout_sec to at least 3 hours so the long-running listen() tool can wait for follow-ups. The adapter is intentionally conservative: Claude SSE config is rejected, only command and streamable HTTP url servers are translated, and environment placeholders are supported only in env/header values that can stay out of process argv.

OpenCode sessions receive the full config via OPENCODE_CONFIG_CONTENT (inline JSON). The .mcp.json adapter translates command-based servers to OpenCode's local type (merging command+args into a single array) and url-based servers to remote type, renaming env to environment. Each translated server gets a 3-hour MCP timeout so listen() can block for follow-ups; listen() sends MCP progress notifications during its long-poll loop, which OpenCode uses to reset its per-call timeout. The system prompt file path is passed via the instructions config field.

The listener is deliberately minimal. It omits concurrency caps, workspace isolation, staleness watchdogs, and dashboards -- add those when you need them.

Key Design Details

Listening for messages

The listen tool uses Zulip's real-time events API (long-polling) instead of repeated GET /messages calls. On entry it catches up on any messages since last_seen_message_id, subscribes the bot to the stream if needed, registers a narrowed event queue for the stream/topic, and then long-polls via GET /events. The server blocks until a message arrives or ~90 seconds elapse (heartbeat), making this ~30x more efficient than polling every 2 seconds. If the queue expires (BAD_EVENT_QUEUE_ID), it re-registers automatically. The queue is deleted in a finally block on exit.

A robot_ear emoji is added to the last message as a visual indicator while listening and removed when listening stops. MCP keepalive pings are sent via ctx.info() after each long-poll cycle.

No missed messages on reply

When reply is called, it checks for new messages before sending. If anyone posted while the LLM was thinking, those messages are fetched and returned alongside the "message sent" confirmation. This way the LLM always sees what it missed and can react accordingly. The last_seen_message_id is updated to whichever is newest -- the missed messages or the sent message -- so nothing falls through the cracks.

Session dismissal

Users can dismiss a bot session by reacting with a configurable emoji (default: :stop_sign:) on any bot message. The dismiss check runs both during listen() (via reaction events) and before reply() (via REST API poll), covering the race condition where a user reacts while the bot is busy working. Customize with configure(dismiss_emoji={"stop_sign", "wave"}).

Bot visibility filtering

Topics containing /nobots or /nb are hidden from the bot entirely. Messages starting with /nobots or /nb are also filtered out. This lets humans have private conversations the bot won't see.

Environment Variables

License

MIT