Z
zhuyansen/new-term-radar
New model and product names before keyword tools
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
- Copy the command above
- Paste it as one entry under
mcpServersinclaude_desktop_config.json— local servers take command + args (example: clients) - Restart your client
Client config (built from the install command this repo gives)
{
"mcpServers": {
"zhuyansen-new-term-radar": {
"command": "uvx",
"args": ["new-term-radar"]
}
}
}Parsed from the README we fetched on 2026-11-04; we have not executed it.
command/args come from the install command the repository states. When it is ambiguous, the project README wins.
VS Code (.vscode/mcp.json - the key is servers)
{
"servers": {
"zhuyansen-new-term-radar": {
"command": "uvx",
"args": ["new-term-radar"]
}
}
}Same server, but VS Code keeps it under <code>servers</code>.
Claude Code (command line)
claude mcp add --transport stdio zhuyansen-new-term-radar -- uvx new-term-radar
Claude Code does not read a config file; add the server with this command.
Key names differ by client: Clients & CLI
- Source
- collected from the abordage/awesome-mcp list · type: MCP server
- Harvest history
- first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 10 sources
- Score
- score 1,003 · 10 sources · mcp
- Install
uvx new-term-radar
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
uvx new-term-radar. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-11-05；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.
# new-term-radar
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Catch the names AI people are about to search for, the day they appear, before keyword tools list them.
⭐ 700+ stars on its first day, 1,000 in just over three · on PyPI, the official MCP Registry, Cursor Directory, LobeHub, Tessl, ClawHub and skills.sh
Quick start · What it reads · The keyword table · Agents and MCP · config.json · 中文说明
A daily report of the names that are starting to surge in AI: new models, products, tools and repos, caught where they are said first, before they show up in keyword tools.
Built for people who make pages, posts or products around new terms: SEO for AI tool sites, newsletters, directories, anyone who wants to know about "Strata" or "Seedance" the day it appears and not two weeks later.
## Why
A new name leaves a trail, in this order:
1. A lab or a well-followed account says it on X; a model enters Hugging Face's trending list.
2. One GitHub repo takes off on its own.
3. Directories and competitors add pages for it; someone registers a domain for it and the site starts to rank.
4. Your own site gets its first search impressions for it.
5. Google Trends shows the curve, days or weeks later.
Keyword tools read step 5. This radar reads steps 1 to 4 every day and uses step 5 only to check itself.
One example it was built around: Niko1221/Strata was created on 2026-09-24 and had 7,700 stars nine days later. People were searching "niko strata" from 9/26, while no second repo carried the name and no keyword tool listed it. A single repo gaining 800 stars a day is the whole signal, and it is public.
## What it reads
Purple, tagged NEW: what 0.6.0 added. On the keyword table: word age, markets, page-one shape, the twelve months, your own rank, what else holds page one (threads, an AI overview) and the exact-match .com. The first words ③'s new sites rank for, what ChatGPT recommends for ⑦'s asks, three more payment platforms and the builders on ⑧, ⑨ just listed, ⑩ money and the evidence beside the names. All of it is in CHANGELOG.md.
| Signal | What it catches | Needs | Cost |
| ① X | Names the accounts in your list used for the first time in 60 days | AIsa key | $0.003 per account per run |
| ① Hugging Face | Models and Spaces (the demos people try) new to the trending top 50; quantised copies and a model's own demo grouped under the base name | nothing | free |
| ② GitHub breakout | A repo under 14 days old averaging 200+ stars a day, or +400 since yesterday; adds the search variants people use ("niko strata") | nothing (a GitHub token raises the rate limit) | free |
| ③ Sitemaps | Words that several new URLs on a site share and its older URLs rarely carried. The example config watches eight AI tool directories (toolify, aixploria, ai-bot.cn, opentools, dang.ai, clipfly, aitoptools, futurepedia) and four MCP and skill directories | the sitemaps you list | free |
| ③ New sites | Domains registered and first indexed in the last 90 days that already rank for 20+ keywords, each new one with the first words it ranks for in Google's top 20, in any country: searches a site that new already reached | AIsa key | about $0.13 per query, weekly; about $0.013 a new site for its words |
⭐ 700+ stars on its first day, 1,000 in just over three · on PyPI, the official MCP Registry, Cursor Directory, LobeHub, Tessl, ClawHub and skills.sh
Quick start · What it reads · The keyword table · Agents and MCP · config.json · 中文说明
A daily report of the names that are starting to surge in AI: new models, products, tools and repos, caught where they are said first, before they show up in keyword tools.
Built for people who make pages, posts or products around new terms: SEO for AI tool sites, newsletters, directories, anyone who wants to know about "Strata" or "Seedance" the day it appears and not two weeks later.
## Why
A new name leaves a trail, in this order:
1. A lab or a well-followed account says it on X; a model enters Hugging Face's trending list.
2. One GitHub repo takes off on its own.
3. Directories and competitors add pages for it; someone registers a domain for it and the site starts to rank.
4. Your own site gets its first search impressions for it.
5. Google Trends shows the curve, days or weeks later.
Keyword tools read step 5. This radar reads steps 1 to 4 every day and uses step 5 only to check itself.
One example it was built around: Niko1221/Strata was created on 2026-09-24 and had 7,700 stars nine days later. People were searching "niko strata" from 9/26, while no second repo carried the name and no keyword tool listed it. A single repo gaining 800 stars a day is the whole signal, and it is public.
## What it reads
Purple, tagged NEW: what 0.6.0 added. On the keyword table: word age, markets, page-one shape, the twelve months, your own rank, what else holds page one (threads, an AI overview) and the exact-match .com. The first words ③'s new sites rank for, what ChatGPT recommends for ⑦'s asks, three more payment platforms and the builders on ⑧, ⑨ just listed, ⑩ money and the evidence beside the names. All of it is in CHANGELOG.md.
| Signal | What it catches | Needs | Cost |
| ① X | Names the accounts in your list used for the first time in 60 days | AIsa key | $0.003 per account per run |
| ① Hugging Face | Models and Spaces (the demos people try) new to the trending top 50; quantised copies and a model's own demo grouped under the base name | nothing | free |
| ② GitHub breakout | A repo under 14 days old averaging 200+ stars a day, or +400 since yesterday; adds the search variants people use ("niko strata") | nothing (a GitHub token raises the rate limit) | free |
| ③ Sitemaps | Words that several new URLs on a site share and its older URLs rarely carried. The example config watches eight AI tool directories (toolify, aixploria, ai-bot.cn, opentools, dang.ai, clipfly, aitoptools, futurepedia) and four MCP and skill directories | the sitemaps you list | free |
| ③ New sites | Domains registered and first indexed in the last 90 days that already rank for 20+ keywords, each new one with the first words it ranks for in Google's top 20, in any country: searches a site that new already reached | AIsa key | about $0.13 per query, weekly; about $0.013 a new site for its words |
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