Server data from the Official MCP Registry
AI Briefing MCP — Keep AI models current on industry developments
About
AI Briefing MCP — Keep AI models current on industry developments
Remote endpoints: streamable-http: https://gateway.pipeworx.io/ai-briefing/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. Trust signals: trusted author (119/119 approved). 1 finding(s) downgraded by scanner intelligence.
9 tools verified · Open access · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-pipeworx-io-ai-briefing": {
"url": "https://gateway.pipeworx.io/ai-briefing/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
mcp-ai-briefing
AI Briefing MCP — Keep AI models current on industry developments
Part of Pipeworx — an MCP gateway connecting AI agents to 1476+ live data sources.
Tools
| Tool | Description |
|---|---|
get_briefing | Get the daily AI tools digest for a given date (default: today) — new MCP servers, APIs, SDKs, and frameworks released in the last 24 hours, with summaries and source URLs. |
search_developments | Search for new tools, APIs, MCP servers, and frameworks by keyword (e.g., 'vector databases', 'Claude integrations'). Returns matching developments with descriptions and sources. |
get_recent | Retrieve AI developments from the last N days (default 7), filterable by category (e.g., model_release, paper, mcp), source (e.g., arxiv, github), and importance (low/normal/high/breaking). |
get_model_landscape | List AI model releases from the last N days (default 30). Returns model names, provider companies, release dates, feature summaries, and source URLs grouped by importance. |
get_timeline | Get a chronological timeline of AI developments between two dates. Returns events ordered by date with descriptions for understanding a specific period. |
get_ai_toolbelt | Get the latest available tools — Claude Code features, MCP servers, SDK updates, CLI tools, integrations. Returns new capabilities since your training cutoff. |
get_ai_news | Get AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns news events with dates and summaries for industry context. |
what_happened | Ask natural language questions about recent tools and developments (e.g., 'any new MCP servers this week', 'latest Claude tools'). Returns the most relevant developments. |
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"ai-briefing": {
"url": "https://gateway.pipeworx.io/ai-briefing/mcp"
}
}
}
What this endpoint actually serves
tools/list at https://gateway.pipeworx.io/ai-briefing/mcp returns the tools in the table
above plus the shared Pipeworx meta-tools — ask_pipeworx,
discover_tools, search_within, remember/recall and the rest of the
gateway-wide set. So the tool count you see is larger than this table: a
single-pack endpoint currently lists roughly 30 shared tools alongside the
pack's own. The connection's initialize response states its exact scope, and
is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a
scoped connection answer a question this pack does not cover — via
ask_pipeworx, which routes across the whole catalog — without you adding a
second MCP server. There is currently no way to mount a pack endpoint without
them; if the extra schemas cost you more context than the routing is worth,
connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}
Both URLs reach the same gateway and the same 1476+ data sources. The
only difference is which pack's tools are listed directly; ask_pipeworx
reaches all of them from either one.
Using with ask_pipeworx
Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:
ask_pipeworx({ question: "your question about Ai Briefing data" })
The gateway picks the right tool and fills the arguments automatically.
More
License
MIT
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