Plug brickster.ai into your LLM.

brickster.ai's MCP gives Claude Desktop, Cursor, and any other Model Context Protocol client typed tools over the curated archive — semantic search across everything, plus recency-sorted reads of releases, news, videos, and the reading list. No key, no install, no leaving the chat.

Install

Drop the snippet into your client's MCP config and restart the client. Per-client instructions live in the MCP docs.

{
  "mcpServers": {
    "brickster": {
      "type": "http",
      "url": "https://brickster.ai/api/mcp"
    }
  }
}

Works in clients that support remote MCP transports natively (Claude Desktop's custom connectors, Cursor, etc.). Stdio-only clients can wrap it via npx mcp-remote https://brickster.ai/api/mcp.

Tools

• search_archive
  (query, limit?, source_types?)

  Semantic search over the entire archive — videos, releases, news, projects, community Q&A. Returns ranked matches with title, URL, source kind, and excerpt.

• list_recent_releases
  (days?, limit?, repo?)

  Recent GitHub releases for repos in the Databricks ecosystem (databricks-sdk-*, dbt-databricks, mlflow, delta-io/delta, the Terraform provider, etc.).

• list_recent_news
  (days?, limit?, source?)

  Recent articles from the Databricks blog and ecosystem feeds, with LLM summaries when available.

• list_recent_videos
  (days?, limit?, channel_handle?)

  Recent YouTube uploads from the official Databricks channel and a curated set of community creators.

• recommend_books
  (topic?)

  Curated reading list for the Databricks ecosystem, optionally filtered by topic tag.

What you can ask

• “What's new in MLflow this week?”

  → list_recent_releases(repo: "mlflow/mlflow", days: 7)

• “Find videos that explain Photon's vectorisation.”

  → search_archive(query: "Photon vectorisation", source_types: ["video"])

• “Recommend a book for getting started with Delta Lake.”

  → recommend_books(topic: "Delta Lake")