xiaozhi-mcp: Instructions file for Codex

AGENTS.md

xiaozhi-mcp AGENTS.md is an instructions file for Codex, OpenCode from botwith/xiaozhi-mcp. It costs 1,305 tokens per session, scanned A, original, MIT.

A repository guide for AI coding agents working on xiaozhi-mcp, a Python project that provides tool servers for tasks such as calculations, email, stock queries, and todos. It documents setup, running, testing, and coding conventions.

In plain words
What is it for?
Installing dependencies with uv or pip, running the application or Docker version, checking the available test setup, and following the project's development guidance.
Why use it?
It gives an agent project-specific instructions so it can use the expected dependency and run commands while working in the repository.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md.

This is botwith/xiaozhi-mcp's own configuration. It tells Codex and OpenCode how to work on xiaozhi-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything xiaozhi-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to botwith/xiaozhi-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/botwith/xiaozhi-mcp/dev/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/botwith/xiaozhi-mcp

Made for: Codex, OpenCode.

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Per session 1,305 This file is loaded in full into every session.
When invoked 1,305 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.01305 $0.01305
Opus 5.5 $0.00522 $0.00522
Sonnet 5.5 $0.00261 $0.00261
Haiku 4.5 $0.00130 $0.00130

Measured 2mo ago against content hash 8138b53eac13, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

xiaozhi-mcp AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 162 lines

How it starts

The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md - Guide for Agentic Coding

This document contains essential information for AI agents working on this repository.

Project Overview

xiaozhi-mcp is a Python-based MCP (Model Context Protocol) project that provides tool servers for AI capabilities including calculations, email, stock queries, todo management, and more. The project uses FastMCP to create stdio-based MCP servers.

Build/Install/Test Commands

Installation

# Recommended: Use uv for dependency management
uv sync

# Alternative: Use pip
pip install -r requirements.txt

Running the Application

# Run all configured servers from mcp_config.yaml
uv run python src/app.py

# Run a single server script
uv run python src/app.py src/mcp/tools/calculator.py

# Run with Docker
docker build -f docker/Dockerfile -t xiaozhi-mcp .
docker run -d --name xiaozhi-mcp -v $(pwd)/mcp_config.yaml:/app/mcp_config.yaml:ro xiaozhi-mcp

Testing

# No test framework is currently configured
# If pytest is added: pytest tests/; pytest tests/test_file.py::test_func

Linting/Type Checking

# No commands configured in pyproject.toml
# Common tools: ruff check ., ruff format ., mypy src/

Code Style Guidelines

File Structure

  • Main application: src/app.py
  • MCP tools: src/mcp/tools/*.py
  • Each tool is a standalone FastMCP server with @mcp.tool() decorated functions
  • Configuration: mcp_config.yaml (YAML format)

Imports

# Standard import order:
from fastmcp import FastMCP
import sys
import logging
# Third-party imports
import yaml
import json
from datetime import datetime
from typing import Optional, Dict, List, Any
# Local imports (if any)
from pathlib import Path
import uuid

Module Layout

# 1. File docstring (optional)
# 2. Imports
# 3. Windows UTF-8 encoding fix
if sys.platform == 'win32':
    sys.stderr.reconfigure(encoding='utf-8')
    sys.stdout.reconfigure(encoding='utf-8')
# 4. Logger setup
logger = logging.getLogger('ServerName')
# 5. Create MCP server
mcp = FastMCP("ServerName")
# 6. Constants (UPPER_CASE)
CONSTANT_NAME = "value"
# 7. Helper functions
# 8. Tool functions (@mcp.tool())
# 9. Main entry point
if __name__ == "__main__":
    mcp.run(transport="stdio")

Read the full file on GitHub · 162 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 162 lines · 1,305 tokens per session scan A 8138b53eac13

Subscribe to this mod's changes

xiaozhi-mcp AGENTS.md is an instructions file published in the GitHub repository botwith/xiaozhi-mcp (0 stars, last pushed 9mo ago), licensed MIT. It adds 1,305 tokens to every session, about $0.0052 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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