mcp-qweather: Instructions file for Codex

AGENTS.md

mcp-qweather AGENTS.md is an instructions file for Codex, OpenCode from xjtuwangke/mcp-qweather. It costs 2,017 tokens per session, scanned A, original, MIT.

Project instructions for an MCP weather server that connects AI assistants to the QWeather weather service. They describe the project structure, Python setup, commands, and API architecture.

In plain words
What is it for?
Developing, testing, and maintaining tools for city lookup, current and forecast weather, precipitation, air quality, astronomy, and weather-related indices.
Why use it?
They give contributors a shared reference for how the weather server is organized and how its authentication and endpoints work.

Instructions file for CodexOpenCode

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

This is xjtuwangke/mcp-qweather's own configuration. It tells Codex and OpenCode how to work on mcp-qweather 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 mcp-qweather configures →

Reuse

Borrowing it

Nothing to install: this file belongs to xjtuwangke/mcp-qweather. 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/xjtuwangke/mcp-qweather/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/xjtuwangke/mcp-qweather

Made for: Codex, OpenCode.

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Per session 2,017 This file is loaded in full into every session.
When invoked 2,017 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 $0.02017 $0.02017
Opus 5 $0.01009 $0.01009
Sonnet 5 $0.00403 $0.00403
Haiku 4.5 $0.00202 $0.00202

Measured 9d ago against content hash e4f1f00ee5d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

mcp-qweather 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 9d 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 · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — MCP QWeather Server

Project Overview

A Weather MCP (Model Context Protocol) Server built with FastMCP that wraps the QWeather (和风天气) API into standardized MCP tools for AI assistants (Claude, etc.).

  • Language: Python 3.13+
  • Package manager: uv
  • Framework: FastMCP
  • License: MIT

Project Structure

.
├── apis/                        # Core API client library
│   ├── __init__.py              # Exports GeoAPI, WeatherAPI, MinutelyAPI
│   ├── base.py                  # QWeatherAPI base class, JWT auth, 17 validators
│   ├── geo.py                   # GeoAPI — city_lookup, poi_lookup, poi_range
│   ├── weather.py               # WeatherAPI — 6 weather endpoints (now/daily/hourly/grid)
│   ├── minutely.py              # MinutelyAPI — precipitation, AQI, astronomy, indices
│   └── schemas.py               # Empty placeholder for Pydantic models
├── keys/
│   ├── ed25519-public.pem       # Ed25519 public key (committed)
│   └── ed25519-private.pem      # Private key (gitignored)
├── tests/
│   └── test_api.py              # Comprehensive test suite (~61 cases)
├── server.py                    # MCP server entry point — 18 tools, 2 transport modes
├── config.py                    # pydantic-settings configuration from .env
├── test_client.py               # Simple MCP client example
├── pyproject.toml               # uv project config, 5 deps
├── uv.lock                      # Locked dependency versions
├── .dockerignore                # Docker build exclusions
├── Dockerfile                   # Python 3.13-slim, HTTP mode
├── docker-compose.yaml          # Port 28001:8000, secret mount, .env
├── docker-run.sh                # 203-line bash deploy script
├── .env.example                 # Environment variable template
└── .gitignore                   # Excludes __pycache__, .venv, .env, private keys

Commands

Command Purpose
uv sync Install dependencies
uv run python server.py --stdio Run server in stdio mode
uv run python server.py Run server in HTTP mode (0.0.0.0:8000, default for Docker)
uv run python tests/test_api.py Run full test suite (~61 tests)
./docker-run.sh --detach Build & run Docker container
docker-compose up -d Run via Docker Compose

Read the full file on GitHub · 168 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. 9d ago First seen · 168 lines · 2,017 tokens per session scan A e4f1f00ee5d0

Subscribe to this mod's changes

mcp-qweather AGENTS.md is an instructions file published in the GitHub repository xjtuwangke/mcp-qweather (0 stars, last pushed 4mo ago), licensed MIT. It adds 2,017 tokens to every session, about $0.0101 per session on Opus 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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