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6 MCP servers that help AI developers go from "bronze" to "king" in seconds

Written by

Jasper Cole

Updated on:June-13th-2025

In the past two months, I have immersively tested more than 100 MCP servers. This type of tool is like installing a "physical joystick" on AI, allowing large models such as ChatGPT and Claude to break through the limitations of pure text and truly reach the physical world such as web pages, files, and databases.

The following 6 tools can be called the " development efficiency improvement combo ". Each one comes with a tested code snippet to teach you how to unlock the "hands-on ability" of AI.

Bright Data MCP: Smoothly collect web data

Bright Data is a powerful tool for crawling web data. It comes with about 30 different tools to crawl websites, search for information, etc., and is smart enough to avoid blockages and verification codes . I used it to crawl the prices of 10 sneaker websites, which is 80% faster than manual operation, and it can also automatically process sliding verification codes.

Core capabilities : bypass anti-crawling mechanisms and accurately capture dynamic web page data

Practical scenarios : e-commerce competitor price monitoring, academic data crawling, ML training set construction

# Tell your AI to grab stuff from a siteCommand: "Get prices from https://sneaker-store.com"

# Bright Data picks the right toolTool: Web ScraperOutput:- Air Zoom: $99- Retro High: $120

GitHub address:

https://github.com/brightdata/brightdata-mcp

Graphiti MCP: Long-term memory for AI

Unlike most AIs, which clear their memory after completing a task, Graphiti solves this problem by letting your AI store information in a knowledge graph, a bit like putting sticky notes on a robot. I've used it to let Claude track user preferences, and it's very convenient. If you need your AI to remember some information, such as whether someone likes dark mode, this is the tool you're looking for.

Core capabilities : Storing context through knowledge graphs to solve the "amnesia" problem of large models

Practical scenarios : user preference management, conversation history tracking, complex process status recording

# Save somethingCommand: "Note that Alex likes dark mode" Graphiti Action: Adds to the graph- Alex -> Likes -> Dark Mode (May 27, 2025)

# Check laterCommand: "Does Alex like dark mode?" Output: Yeah, Alex is Team Dark Mode!

GitHub address:

https://github.com/getzep/graphiti/tree/main/mcp_server

GitIngest MCP: GitHub’s AI Navigator

GitIngest is my favorite for GitHub-related stuff. It gives your AI two tools: one for viewing the layout of your repository, and another for reading files. This tool saved me from a meltdown the other day when I was exploring a large project with a lot of files.

Core capabilities : remote analysis of warehouse structure, rapid location of code files Practical scenarios : open source project research, code auditing, cross-team collaborative code review

# Check a repoCommand: "What's the setup of https://github.com/awesome/project?"GitIngest Tool: git_directory_structureOutput:- code/ - script.js- docs/ - readme.md

# Read a fileCommand: "Show me the readme.md"Output: [readme stuff]

GitHub address:

https://github.com/puravparab/Gitingest-MCP

Terminal MCP: AI Butler for Terminals

Terminal MCP (or Desktop Commander MCP) allows your AI to take over the command line. It can find files, move things around, run scripts, and basically do anything you would do in the terminal. I used it to clean up a cluttered folder full of old code, and it felt like having a personal assistant.

Core capabilities : Control command lines through natural language, freeing your hands

Practical scenarios : batch file processing, script automation execution, server operation and maintenance

# Find filesCommand: "Show me all Python files in my folder"Terminal MCP Tool: SearchOutput:- app.py- helper.py

# Run somethingCommand: "Run app.py"Output: [whatever the script does]

GitHub address: https://github.com/wonderwhy-er/DesktopCommanderMCP

Code Executor MCP: An AI Sandbox for Python

Code Executor MCP allows your AI to run Python code in a Conda environment and supports all installed libraries. Last week, when I was debugging a data script, I used it to test it directly without setting up a new environment, which was very convenient.

Core capabilities : Run Python code in an isolated environment and support Conda dependencies

Practical scenarios : fast algorithm verification, data script debugging, lightweight task execution

# Tell your AI to run some codeCommand: "Run this Python code"

Code:import numpy as npnums = np.array([5, 10, 15])print(nums.mean())Output: 10.0

GitHub address:

https://github.com/bazinga012/mcp_code_executor

MindsDB MCP: AI Translator of Data

The last one is MindsDB. This server connects all your data: Slack, Gmail, databases, whatever, and lets your AI dig into that data. I’ve used it to extract work-related feedback from Slack much faster than I could do manually.

Core capabilities : unified connection of multi-source data, support for natural language/SQL queries

Practical scenarios : cross-platform data analysis, enterprise data center, and automated report generation

# Grab Slack feedbackCommand: "What's the latest feedback on Slack?"MindsDB Action: Pulls dataOutput:- Sarah: "This rocks!"- Mike: "Eh, needs work."

# Try SQLCommand: "SELECT * FROM feedback WHERE vibe = 'positive'"Output: [happy feedback]