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Independent project, not affiliated with Anthropic
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Ai Briefing

pipeworx-io/mcp-ai-briefing
HTTPregistry active
Summary

Pulls daily briefings on new AI tools, MCP servers, APIs, and frameworks released in the last 24 hours so you can keep models informed about developments after their training cutoff. You get eight tools: fetch today's briefing at session start, search by keyword for specific tech like vector databases or Claude integrations, filter by category or source, check recent model releases across providers, query chronological timelines between dates, and ask natural language questions about what shipped recently. Runs on Pipeworx's gateway infrastructure with 673+ data sources behind it. Useful when you need an AI assistant to know about tooling and releases it wouldn't have seen during training.

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mcp-ai-briefing

AI Briefing MCP — Keep AI models current on industry developments

Part of Pipeworx — an MCP gateway connecting AI agents to 1786+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.

Tools

ToolDescription
get_briefingGet 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_developmentsSearch for new tools, APIs, MCP servers, and frameworks by keyword (e.g., 'vector databases', 'Claude integrations'). Returns matching developments with descriptions and sources.
get_recentRetrieve 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). The category is a keyword label derived from the headline at ingest, not a curated classification — filtering by it narrows the feed but does not guarantee every row belongs; the response repeats this caveat whenever a category filter is applied.
get_model_landscapeList 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. Membership comes from the same heuristic headline label as get_recent(category=model_release), so verify the titles before quoting the list as complete.
get_timelineGet a chronological timeline of AI developments between two dates. Returns events ordered by date with descriptions for understanding a specific period.
get_ai_toolbeltGet the latest available tools — Claude Code features, MCP servers, SDK updates, CLI tools, integrations. Returns new capabilities since your training cutoff.
get_ai_newsGet AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns news events with dates and summaries for industry context.
what_happenedAsk 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 1786+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

No MCP client? Call it over HTTP

curl -X POST https://gateway.pipeworx.io/v1/tools/get_briefing \
  -H 'Content-Type: application/json' \
  -d '{}'

No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/get_briefing. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.

Standalone (no gateway account)

This package also runs as a local stdio MCP server — no Pipeworx account, no gateway round-trip:

{
  "mcpServers": {
    "ai-briefing": {
      "command": "npx",
      "args": ["-y", "@pipeworx/mcp-ai-briefing"]
    }
  }
}

Or run it directly to confirm it starts:

npx -y @pipeworx/mcp-ai-briefing

It speaks MCP over stdin/stdout and answers initialize/tools/list/tools/call for only this pack's tools — none of the shared meta-tools the gateway connection above adds. Same source, same tools, no ask_pipeworx routing.

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

  • Docs and guides
  • pipeworx.io

License

MIT

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Registryactive
TransportHTTP
UpdatedApr 22, 2026
View on GitHub

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