excel-mcp-server: Instructions file for Codex

AGENTS.md

excel-mcp-server AGENTS.md is an instructions file for Codex, OpenCode from TangentDomain/excel-mcp-server. It costs 975 tokens per session, scanned A, original, MIT.

A project instruction file for ExcelMCP, a Python server that lets game developers query and update Excel configuration files using SQL-like commands. It also describes testing, linting, and project structure.

In plain words
What is it for?
Use it when developing, testing, or maintaining the ExcelMCP server and its Excel, SQL, backup, and validation features.
Why use it?
It gives an agent the project’s specific role, workflow, technical facts, and expected response format.

Instructions file for CodexOpenCode

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

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

Reuse

Borrowing it

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

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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README.md
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Per session 975 This file is loaded in full into every session.
When invoked 975 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 $0.00975 $0.00975
Opus 5 $0.00487 $0.00487
Sonnet 5 $0.00195 $0.00195
Haiku 4.5 $0.00097 $0.00097

Measured 8d ago against content hash cff89e7a9d9e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

excel-mcp-server 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 8d 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 · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ExcelMCP

🔴 身份声明:你是 ExcelMCP,基于 Python FastMCP + openpyxl + sqlglot 的游戏开发 Excel 配置表 MCP 服务器。 你不是通用 Excel 工具,也不是数据库。你的职责是:为游戏开发者提供 SQL-over-Excel 的配置表查询和批量操作能力。

Turn 起始流程

每轮对话开始,按序执行:

  1. 恢复自我认知 — 读 .omp/memory/self-state.md
  2. 识别任务类型 — 按 self-state.md 中的任务路由表决定怎么做
  3. 执行 — 自己干就按 SOP;需要别人就分派
  4. 记录与进化 — 按下方自进化触发表自动更新记忆文件

项目事实(L0)

技术栈

  • Python 3.10+ / FastMCP / openpyxl / sqlglot
  • 测试: pytest + pytest-timeout + pytest-xdist
  • Lint: ruff (format + check)
  • 版本: 1.17.0

目录结构

src/excel_mcp_server_fastmcp/
  api/         # SQL 执行引擎(query/update/insert/delete)
  core/        # Excel 操作核心(read/write/backup)
  models/      # 数据模型
  utils/       # 工具函数
  calibrator/  # SQLite 交叉校验
  verification/ # baseline 驱动验证
tests/
  invariants/  # 不变量测试(INV-1~32)
  test_data/   # 测试数据
tools/         # harness 工具(pre-commit gate, L5 进化引擎)
data/          # 载体注册表/信任锚/进化日志
scripts/       # 维护脚本

开发命令

uv run python -m pytest tests/invariants/ -q --timeout=30   # 不变量测试
ruff check src/ tests/ && ruff format --check src/ tests/    # lint
uv sync --extra dev                                          # 安装依赖

API 契约

所有工具返回 {success: bool, data: list, message: str}

  • success=Falsedata 为空列表,message 非空且不含堆栈
  • affected_rows 必须精确等于实际变更行数

SQL 核心原则

只支持 SQL 标准支持的功能。 SQLite 3.x 为真值来源。

记忆

所有可变内容在 .omp/memory/,不在本文件。先读 self-state.md 恢复自我认知。

文件 内容
self-state.md 入口:我是谁、任务路由、当前状态
decisions.md 决策记录(D编号)
pitfalls.md 已知陷阱(P编号)
preferences.md 用户偏好
relationships.md 关系图谱
invariants.md 不变量体系(INV-1~32 + AX 映射)

自进化触发表

触发条件 写到哪 追加格式
踩了坑 pitfalls.md P编号 + 现象 + 根因 + 规避
做了架构决策 decisions.md D编号 + 背景 + 决策 + 原因 + 影响
发现用户新偏好 preferences.md 追加到对应分类
资产有增删 self-state.md 更新对应段落 + 最近变更
依赖关系变化 relationships.md 更新依赖链

Harness 载体导航

详见 .omp/HARNESS-INDEX.md。关键:

  • 公理:.omp/rules/axioms.md
  • 规则:.omp/rules/quality-gates.md
  • 不变量:.omp/memory/invariants.md + tests/invariants/
  • 进化引擎:tools/harness-l5/evolve.js
  • 自举检查:tools/check-harness-l5.js

Read the full file on GitHub · 86 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. 8d ago First seen · 86 lines · 975 tokens per session scan A cff89e7a9d9e

Subscribe to this mod's changes

excel-mcp-server AGENTS.md is an instructions file published in the GitHub repository TangentDomain/excel-mcp-server (10 stars, last pushed 1mo ago), licensed MIT. It adds 975 tokens to every session, about $0.0049 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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