VLM-mcp: Instructions file for Codex

AGENTS.md

VLM-mcp AGENTS.md is an instructions file for Codex, OpenCode from YC-CLT/VLM-mcp. It costs 2,002 tokens per session, scanned A, original, MIT.

A set of project instructions for VLM-mcp, a local server that lets an agent work with image-understanding models. It documents the project setup, important files, configuration, caching, sessions, and commands.

In plain words
What is it for?
Use it when working on the VLM-mcp Python project, especially its server, model launcher, configuration, sessions, caches, and image handling.
Why use it?
It gives an agent the project-specific rules needed to navigate and modify the code consistently.

Instructions file for CodexOpenCode

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

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

Reuse

Borrowing it

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

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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Per session 2,002 This file is loaded in full into every session.
When invoked 2,002 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 $0.02002 $0.02002
Opus 5.5 $0.00801 $0.00801
Sonnet 5.5 $0.00400 $0.00400
Haiku 4.5 $0.00200 $0.00200

Measured 1mo ago against content hash a75ed2776258, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

VLM-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 1mo 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 · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

环境

  • Python 3.11,uv 管理依赖
  • config.py 集中配置所有常量,pyproject.toml 管理依赖
  • 命令:用 cmd-exec-mcp

关键文件

文件 作用
main.py 入口,纯启动脚本,启动 MCP
server.py MCP Server,7 个工具,stdio 传输
config.py 所有常量 + config.json 加载
config.json 敏感配置(API Key、模型路径),不入 git
llama_launcher.py llama-server 子进程生命周期(启/停/健康检查)
providers/ocr_provider.py OCR Provider(RapidOCR 懒加载 + 延迟卸载)
providers/openai_compat.py OpenAI 兼容 API 客户端(AsyncOpenAI)+ llama 懒启动
session_manager.py 会话 CRUD + 超时清理 + 满时驱逐
cache.py L1 图片缓存(LRU)+ L2 响应缓存(LRU + TTL)
image_utils.py 图片源解析(路径/URL/Data URI/Base64 回退)
logger.py 单例 logger → log.txt

关键常量

常量 位置 说明
IMAGE_MAX_SIZE_MB config.py 图片最大体积 (20MB)
IMAGE_DOWNLOAD_TIMEOUT config.py 图片下载超时秒数 (10s)
CACHE_IMAGE_MAX_ENTRIES config.py L1 缓存上限 (100)
CACHE_RESPONSE_MAX_ENTRIES config.py L2 缓存上限 (500)
CACHE_RESPONSE_TTL_ONLINE config.py 在线后端缓存 TTL (3600s)
CACHE_RESPONSE_TTL_LOCAL config.py 本地后端缓存 TTL (1800s)
SESSION_TTL config.py 会话超时秒数 (1800s)
SESSION_MAX config.py 每后端最大会话数 (5)
LOG_LEVEL config.py 日志级别 ("INFO")
LLAMA_DEFAULTS config.py llama-server 启动参数默认值
BACKENDS config.py 从 config.json 加载的后端配置
CACHE_ENABLED config.py 从 config.json 加载的缓存开关

规则

  • monkeypatch 必须用 import config + config.X:from config import X 创建本地副本,monkeypatch 无法穿透;executor 同理 patch executors.模块名.X
  • config 重命名全量 grep:常量改名/移除后搜索所有引用
  • 跨 Task 依赖等待:并行派发时先检查上游产物是否存在

工具

  • 部分工具有相应的skill
  • MCP详见 mcp_tools_summary.csv
  • Read 无法访问 D:\Temp,MCP 长输出需 Copy-Item 到项目根目录,正则替换 \\n 为 \n
  • Edit replace_all 错误:replace_all=True 无法使用。优先用 PowerShell Select-String + 正则做精确替换,或手动逐处 Edit
  • 浏览器操控用 chrome-devtools-edge(Edge CDP),禁止用 cua-driver 操控浏览器
  • WebFetch 无法使用:用 wet-mcp extract

工作流

Read the full file on GitHub · 90 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. 1mo ago Changed · +3 lines · +176 tokens per session a75ed2776258
  2. 2mo ago Changed · +5 lines · +226 tokens per session 21058e7157da
  3. 2mo ago First seen · 82 lines · 1,600 tokens per session scan A 4a427c014cff

Subscribe to this mod's changes

VLM-mcp AGENTS.md is an instructions file published in the GitHub repository YC-CLT/VLM-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 2,002 tokens to every session, about $0.0080 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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