Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add YC-CLT/VLM-mcp --skill using-vlm-mcpgit clone --depth 1 https://github.com/YC-CLT/VLM-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yc-clt/vlm-mcp/using-vlm-mcp)<a href="https://agentmods.dev/skills/yc-clt/vlm-mcp/using-vlm-mcp"><img src="https://agentmods.dev/badge/skills/yc-clt/vlm-mcp/using-vlm-mcp/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yc-clt/vlm-mcp/using-vlm-mcp"><img src="https://agentmods.dev/badge/skills/yc-clt/vlm-mcp/using-vlm-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01104 |
| Opus 5.5 | $0.00008 | $0.00442 |
| Sonnet 5.5 | $0.00004 | $0.00221 |
| Haiku 4.5 | $0.00002 | $0.00110 |
Grade A, and why
using-vlm-mcp 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using VLM-MCP
Overview
通过 VLM-mcp MCP 工具对图片进行单次分析、模板处理或多轮对话。核心原则：无会话必传图，有会话可追问，用完必须关。
触发场景
用户提供图片并要求：描述、分析、OCR 提取文字、图表解读、翻译图中文字、基于图片问答。
OCR 优先用 ocr_image。不要优先用 analyze_image + template: "ocr" ，效果不好再换。
不用此 skill 的场景： 纯文本分析（无图片）、非 VLM-mcp 工具的图片处理。
快速参考
调用方式：run_mcp(server_name="vlm-mcp", tool_name="工具名", args={...})。
image 支持三种格式：本地绝对路径（如 D:/images/photo.png）、URL（https://...）、base64 data URI。
| 工具 | 操作 | args |
|---|---|---|
ocr_image |
本地 OCR | image → [{text, box, confidence}, ...] |
analyze_image |
自由描述 | image, prompt |
analyze_image |
模板描述 | image, template: "describe" |
analyze_image |
模板 OCR | image, template: "ocr" |
analyze_image |
图表解读 | image, template: "chart" |
analyze_image |
翻译图中文字 | image, template: "translate", params: {target_lang} |
analyze_image |
图片问答 | image, template: "qa", params: {question} |
analyze_image |
指定后端 | image, prompt, backend |
create_session |
创建会话 | backend（可选）→ {session_id} |
close_session |
关闭会话 | session_id |
list_sessions |
活跃会话 | 无 |
list_backends_tool |
可用后端 | 无 |
list_templates |
可用模板 | 无 |
analyze_image 返回：{text, model, tokens_used, cache_hit, session_id}。
使用模式
模板分析
先用 list_templates 查看可用模板，再按上方快速参考表调用（独立调用，无需 session）。
多轮对话
s = run_mcp("vlm-mcp", "create_session", args={"backend": "dashscope"}) # 可选 backend
sid = s["session_id"]
run_mcp("vlm-mcp", "analyze_image", args={"image": "doc.png", "prompt": "总结", "session_id": sid})
run_mcp("vlm-mcp", "analyze_image", args={"prompt": "第三节？", "session_id": sid}) # 追问可不传图
run_mcp("vlm-mcp", "close_session", args={"session_id": sid}) # 用完必须关
常见错误
| 错误 | 现象 | 正确做法 |
|---|---|---|
无会话忘传 image |
INVALID_PARAMS: image is required |
无会话时 image 必传 |
| 不关会话 | 占槽位（每后端最多 5 个），后续创建报 SESSION_FULL |
用完 close_session；若已占满，list_sessions 查孤儿 → 逐一 close_session |
同时传 template 和 prompt |
template 优先，prompt 被忽略 |
只传其中一个 |
| 会话内换后端 | SESSION_BACKEND_MISMATCH |
新建 session 换后端 |
image 用相对路径 |
IMAGE_NOT_FOUND |
用绝对路径、URL 或 base64 |
用 analyze_image + template: "ocr" 做简单提取 |
浪费在线 API 额度、延迟高 | 优先用 ocr_image |
| 模板名拼错 | TEMPLATE_NOT_FOUND |
list_templates 查看可用模板名 |
| 忘传模板必填参数 | INVALID_PARAMS: Missing required param |
list_templates 查看模板所需参数 |
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.
- 2mo ago Changed · -30 lines · -14 tokens per session e5a0a8fb1fe4
- 2mo ago First seen · 104 lines · 35 tokens per session scan A 62fc97b833db
using-vlm-mcp is a skill published in the GitHub repository YC-CLT/VLM-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,104 once invoked, about $0.0001 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.
using-vlm-mcp compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| using-vlm-mcpYC-CLT/VLM-mcp | Skill | 0 | 1mo ago | MIT | A | 21 | Claude Code, Codex |
| agent-platform-model-registrygoogle/skills | Skill | 23k | today | Apache-2.0 | A | 60 | Claude Code, Codex |
| foundry-config-setupmicrosoft/agent-framework | Skill | 15k | yesterday | MIT | A | 65 | Claude Code, Codex |
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| training-checkwanshuiyin/Auto-claude-code-research-in-sleep | Skill | 19k | 2d ago | MIT | A | 35 | Claude Code |
| nemo-automodel-launcher-configNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 30 | Claude Code |
| finetuningmicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 132 | Claude Code |