5 Powerful MCP Servers

TODAY’S DAILY DOSE OF DATA SCIENCE

5 Powerful MCP Server

Integrating a tool/API with Agents demands:

• reading docs
• writing code
• updating the code, etc.

To simplify this, platforms now offer MCP servers. Developers can plug them with Agents and use their APIs instantly. We also covered it in a recent newsletter issue (read here).

Below, let's look at 5 incredibly powerful MCP servers.

#1) Firecrawl MCP server

This adds powerful web scraping capabilities to Cursor, Claude, and any other LLM clients using Firecrawl.

Tools include:

• Scraping
• Crawling
• Deep research
• Extracting structured data
• and more

Here’s a demo:

#2) Browserbase MCP server

This allows Agents to initiate a browser session with Browserbase.

Tools include:

• Create browser session
• Navigate to a URL
• Take screenshot
• and more

Here’s a demo:

#3) Opik MCP server

This enables traceability into AI Agents and lets you monitor your LLM applications, by Comet.

Tools include:

• Creating projects
• Enable tracing
• Getting tracing stats
• and more

Here’s a demo:

#4) Brave MCP server

This enables Agents to use the Brave Search API for both web and local search capabilities.

Tools include:

• Brave web search
• Brave local search

#5) Sequential thinking

This enables dynamic and reflective problem-solving through a structured thinking process.

Which ones are your favorite MCP servers? Let us know!

IN CASE YOU MISSED IT

​​KV caching in LLMs, explained visually​

KV caching is a popular technique to speed up LLM inference.

To get some perspective, look at the inference speed difference from our demo:

• with KV caching → 9 seconds
• without KV caching → 40 seconds (~4.5x slower, and this gap grows as more tokens are produced).

The visual explains how it works:

​We covered this in detail in a recent issue here →

ROADMAP

​16 techniques to build real-world RAG systems​

On paper, implementing a RAG system seems simple—connect a vector database, process documents, embed the data, embed the query, query the vector database, and prompt the LLM.

But in practice, turning a prototype into a high-performance application is an entirely different challenge.

We published a two-part guide that covers 16 practical techniques to build real-world RAG systems:

• ​​​​Read part 1 here →​​​​
• ​​​​Read part 2 here →