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Docker Pibox MCP Server

by Psyb0t
Developer ToolsModerate7.0MCP RegistryLocal
Free

Server data from the Official MCP Registry

Self-hosted MCP server: pi-coding-agent tools (run_prompt, file ops) over streamable HTTP.

About

Self-hosted MCP server: pi-coding-agent tools (run_prompt, file ops) over streamable HTTP.

Security Report

7.0
Moderate7.0Low Risk

Valid MCP server (3 strong, 1 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) Imported from the Official MCP Registry.

11 files analyzed · 3 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

file_system

Check that this permission is expected for this type of plugin.

env_vars

Check that this permission is expected for this type of plugin.

Shell Command Execution

Runs commands on your machine. Be cautious — only use if you trust this plugin.

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-psyb0t-pibox": {
      "args": [
        "-y",
        "@psyb0t/pibox"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

docker-pibox

CI version license Docker Pulls

pi-coding-agent inside an aicodebox container. One image, several ways in: interactive shell, one-shot prompt, HTTP API (with an OpenAI-compatible endpoint), MCP server, Telegram bot, and a cron scheduler that fires pi on whatever schedule you want.

You talk to pibox. pibox talks to pi. pi talks to whatever LLM you point it at. Nobody cares about the middle.

Table of Contents

Quick start

# one-shot prompt
docker run --rm \
  -e ANTHROPIC_AUTH_TOKEN=your-token \
  -e ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic \
  -e ANTHROPIC_MODEL=glm-4.6 \
  -v "$PWD/workspace:/workspace" \
  psyb0t/pibox:latest \
  -p "list the files in /workspace"

# API server
docker run -d --network host \
  -e PIBOX_API_MODE=1 \
  -e PIBOX_API_MODE_TOKEN=your-secret \
  -e PIBOX_AVAILABLE_MODELS=glm-4.6,glm-4.5-air \
  -e ANTHROPIC_AUTH_TOKEN=your-token \
  -e ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic \
  -e ANTHROPIC_MODEL=glm-4.6 \
  -v "$PWD/workspace:/workspace" \
  psyb0t/pibox:latest

Modes

Foreground modes (API / Telegram / Cron) are mutually exclusive — except PIBOX_TELEGRAM_MODE=1 + PIBOX_CRON_MODE=1, which run together (cron in-thread inside telegram). API wins if set alongside anything else.

MCP mode (PIBOX_MCP_MODE=1) is independent — it coexists with whatever foreground mode is running. In API mode it's mounted at /mcp on the API port; in other modes it runs as a sidecar uvicorn on its own port.

Each mode has its own page with full setup, env vars, and examples.

API Mode →

Long-lived FastAPI server on :8080. Agent runs (sync, async with run-id polling, cancellable), workspace file upload/download/list/delete with traversal checking, and an OpenAI-compatible chat/completions endpoint with streaming and client-executed tool calling.

environment:
  - PIBOX_API_MODE=1
  - PIBOX_API_MODE_TOKEN=your-secret
  - PIBOX_AVAILABLE_MODELS=glm-4.6,glm-4.5-air

Telegram Mode →

Talk to pi from Telegram. Per-chat isolated workspaces, allowed-chats and per-chat allowed-users gating, file ingestion, [SEND_FILE: path] to get files back, and per-chat /model, /effort, /system_prompt, /append_system_prompt overrides that persist across restarts.

environment:
  - PIBOX_TELEGRAM_MODE=1
  - PIBOX_TELEGRAM_MODE_TOKEN=123456:ABC

Cron Mode →

YAML-defined scheduled jobs on 6-field croniter schedules. Per-run history dirs with meta.json, stdout.log, stderr.log, result.txt, and a "prior run" hint so a job can reference its own history.

environment:
  - PIBOX_CRON_MODE=1
  - PIBOX_CRON_MODE_FILE=/home/aicode/.aicodebox/cron.yaml

MCP Mode →

Exposes run_prompt plus workspace-confined file tools over streamable HTTP, so other agents can drive pi as a tool. Coexists with any foreground mode — mounted at /mcp on the API port in API mode, a sidecar on its own port everywhere else.

environment:
  - PIBOX_MCP_MODE=1
  - PIBOX_MCP_MODE_TOKEN=your-secret

Configuration

Naming convention: PIBOX_<MODE>_MODE=1 is the on/off flag, PIBOX_<MODE>_MODE_<KNOB>=... is its config. Non-mode-scoped vars (workspace, container name, available models) are bare.

The image is built on top of aicodebox, so the equivalent AICODEBOX_* names also work — the entrypoint translates PIBOX_X to AICODEBOX_X when only the pibox-prefixed one is set. If you set both, AICODEBOX_* wins.

Set the upstream endpoint URL, protocol, API key or token, and model with LLM providers. PIBOX_PROVIDER_* supports Pi's documented OpenAI, Anthropic, and Google custom HTTP APIs. The ANTHROPIC_* variables below remain a compatibility path for existing Anthropic-compatible deployments.

Mode flags

VarDefaultWhat it does
PIBOX_API_MODE0Boot the HTTP API server (foreground)
PIBOX_TELEGRAM_MODE0Boot the Telegram bot (foreground)
PIBOX_CRON_MODE0Boot the cron scheduler (foreground; in-thread when telegram is also on)
PIBOX_MCP_MODE0Expose MCP — mounted at /mcp in API mode, or as a sidecar elsewhere

Each mode's own knobs (ports, tokens, config paths, history dirs) live on that mode's page: api.md, telegram.md, cron.md, mcp.md.

Workspace & runtime

VarDefaultWhat it does
PIBOX_WORKSPACE/workspaceRoot workspace dir inside the container
PIBOX_CONTAINER_NAMEaicodeboxUsed to scope per-container state files (auth, etc.)
PIBOX_AVAILABLE_MODELSRequired for API mode. CSV list returned by /openai/v1/models and shown in the telegram /model picker. API mode refuses to boot without it; telegram /model picker degrades to a "set this env var" reply.
PIBOX_AVAILABLE_EFFORTSadapter listOverride the effort/--thinking list shown by the telegram /effort picker (comma-separated)

Auth

For Anthropic-compatible endpoints, use the compatibility variables below. For LiteLLM or another OpenAI-compatible endpoint, use generic provider configuration.

VarPurpose
ANTHROPIC_AUTH_TOKENBearer token (Z.AI, direct Anthropic, etc.)
ANTHROPIC_API_KEYSame thing — pi reads both
ANTHROPIC_BASE_URLEndpoint override (default: https://api.anthropic.com)
ANTHROPIC_MODELDefault model when the caller doesn't specify one

Z.AI's GLM models are fast and cheap for most tasks — ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic + ANTHROPIC_MODEL=glm-4.6 is the recommended default.

pi's thinking levels (--thinking): off, minimal, low, medium, high, xhigh. Exposed as the /effort command in telegram mode and as thinking in API requests.

Agent integrations

The skill works in any agent that reads .agents/skills/, and installs natively in the clients below.

Claude Code

claude plugin marketplace add psyb0t/agents
claude plugin install pibox@psyb0t

Claude Code prompts for the pibox URL and, if auth is enabled, the API and MCP tokens — sensitive values are stored in your OS keychain.

Codex

codex plugin marketplace add psyb0t/agents
codex plugin add pibox@psyb0t

Installed via the marketplace, the skill invokes as $pibox:pibox. Codex also picks the skill up automatically with no install in any repo containing .agents/skills/, where it invokes as plain $pibox.

OpenClaw

The skill is published to ClawHub on every release:

openclaw skills install @psyb0t/pibox

For MCP clients that speak local stdio, the @psyb0t/pibox plugin bridges to the service's /mcp endpoint:

openclaw plugins install clawhub:@psyb0t/pibox

Then set PIBOX_URL (and PIBOX_MCP_MODE_TOKEN if the server was started with MCP auth enabled).

Development

make help   # list targets
make build  # pull the published aicodebox base, build + tag psyb0t/pibox:v<VERSION> and :latest
make test   # run the full e2e suite (needs .env.test)
make clean  # remove built images

VERSION is read from pibox/pyproject.toml. make build pulls the published psyb0t/aicodebox base pinned in the Dockerfile; set SKIP_BASE_PULL=1 to use a locally-built base instead (e.g. from a sibling ../docker-aicodebox checkout), or override with make build BASE_IMAGE=....

Tests

End-to-end tests build the image and run it against a real LLM endpoint. Telegram tests use psyb0t/telethon-plus as a real MTProto userbot.

cp .env.test.example .env.test
$EDITOR .env.test   # fill in ANTHROPIC_* and optionally Coding Plan or Telegram creds
make test

Telegram tests auto-skip if AICODEBOX_TELEGRAM_MODE_TOKEN is empty. The generic-provider tests use both Z.AI Coding Plan endpoints and reuse ANTHROPIC_AUTH_TOKEN unless ZAI_CODING_AUTH_TOKEN is set.

License

WTFPL — see LICENSE. Do what the fuck you want.

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