5 Powerful MCP Servers

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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!

Thanks for reading, and we’ll see you next week!

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P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) that align with such topics.

Here are some of them:

• Learn how to build Agentic systems in an ongoing crash course with 6 parts.

• Learn how to build real-world RAG apps and evaluate and scale them in this crash course.

• Learn sophisticated graph architectures and how to train them on graph data in this crash course.

• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.

• Learn how to run large models on small devices using ​Quantization techniques.

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using ​Conformal Predictions.

• Learn how to identify causal relationships and answer business questions using causal inference in this crash course.

• Learn how to scale and implement ML model training in this practical guide.

• Learn techniques to reliably test new models in production.

• Learn how to build privacy-first ML systems using ​Federated Learning.

• Learn 6 techniques with implementation to compress ML models.

All these resources will help you cultivate key skills that businesses and companies care about the most.