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 agentmods add skills/ppparticle/web-server/web-servernpx skills add PPParticle/web-server --skill web-servergit clone --depth 1 https://github.com/PPParticle/web-serverWhat 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 | $0.00049 | $0.01182 |
| Opus 5 | $0.00024 | $0.00591 |
| Sonnet 5 | $0.00010 | $0.00236 |
| Haiku 4.5 | $0.00005 | $0.00118 |
Grade A, and why
web-server scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **PDF**:read_url 检测到 PDF 会返回交接信号(不解析)。如需 PDF 内容,自己 curl 下载 + Python 读取。 How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Server MCP 使用指南
两个工具,组合使用:搜索 → 读取。
web_search: 搜索关键词
web_search(query, topic?, domains?, num?)
topic="academic"→ 搜 arxiv/dblp/semanticscholar/github(论文+代码)topic="technical"→ 搜 stackoverflow/github/dev.to/MDN(文档+问答)topic="community"→ 搜 reddit/HackerNews(讨论+评测)domains=["site.com"]→ 自定义站点限定(覆盖 topic)- 不设 topic/domains → 全网搜
read_url: 读网页内容
read_url(url, engine?, no_cache?, with_links?)
- 默认
engine="auto":自动识别 GitHub/arXiv/SO/Reddit 走 API(结构化),其他走 Readability,SPA 降级 Playwright engine="playwright":强制浏览器渲染(JS 页/反爬站)no_cache=true:强制刷新with_links=true:末尾附正文链接列表(供你决定是否递归探索)
典型工作流
研究一个技术话题
1. web_search("topic", topic="technical") → 拿到 SO/博客 URL 列表
2. read_url(url) → 读最有价值的 1-2 篇
3. with_links=true → 看正文里的链接,决定是否深入
读论文
1. web_search(..., topic="academic") → 论文 URL 列表(注意 fan-out,见下节)
2. read_url("https://arxiv.org/abs/xxxx") → 结构化 metadata(标题/作者/摘要)
3. 需要全文?read_url("https://arxiv.org/html/xxxx") 或手动下载 PDF
学术搜索:关键词 fan-out 策略(重要)
topic="academic" 走 DBLP / arXiv / Semantic Scholar。DBLP 是论文库,按标题精确匹配关键词(多词 query 是 AND 语义,标题里不全有就返回 0)。所以绝不能只发一个字面 query,要对同一研究主题并行发多个 query 变体,合并去重:
- 同义词替换:
offload / offloading / spill / swap / evict / displace - 存储/介质词:
SSD / disk / NVMe / storage / tier / hierarchy - 缩写展开:
KV cache / KV-cache / key-value cache - 子集拆分:多词 query 拆成 2-3 词子 query 再并行 ——
kv cache offloading storage→kv cache ssd+kv cache disk+kv offloading+kv cache storage(单发原词返回 0,拆开后能命中标题含 SSD/disk 的论文) - 已知系统名:研究某领域时直接搜代表性系统名(KV offload 领域:
mooncake、flexgen、vllm、tutti、infercept) - 目标 venue:按会议名搜(
OSDI / SOSP / ASPLOS / FAST / SIGMOD / ICDE / VLDB / ISCA / MICRO)
流程:并行发 3-6 个 query 变体 → 合并所有结果去重(按 URL)→ 按相关度/年份筛 → read_url 读 1-3 篇高价值论文。
残留缺口:若领域全新且你不知道任何系统名,纯标题搜仍可能漏(标题词汇与你 query 完全正交时)。此时考虑改
domains或全网topic搜补充。
读 GitHub 项目
read_url("https://github.com/owner/repo") → 原始 README(1:1 markdown)
read_url("https://github.com/owner/repo/issues/123") → issue + 评论(API 结构化)
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.
- yesterday First seen · 81 lines · 49 tokens per session scan A 043f6e617eee
web-server is a skill published in the GitHub repository PPParticle/web-server (1 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,182 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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