Model Context Protocol (MCP) Servers – A Comprehensive Guide
Model Context Protocol (MCP) servers are lightweight services that let AI models (like LLMs) securely use external tools and data. They function as bridges between AI and various domains – from coding tools to web scrapers – standardized under a common protocol. Below, we organize notable MCP servers by domain and use case, detailing their key functions, best use cases, and benefits. We then highlight the top MCPs overall (by utility and adoption), and provide a cheat sheet for selecting and managing MCPs effectively. (Note: At most40 MCP servers can be active at once in a development workflow, so choosing the right ones is crucial.)
Software Development & Debugging MCPs
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GitHub MCP Server
Provides an AI access to GitHub repositories via the GitHub API for reading code, searching repositories, managing issues, and performing file operations. Best for AI coding assistants that need to fetch or modify code, review pull requests, generate code snippets in context, or automate version control tasks. Streamlines development by letting the AI directly retrieve and analyze repository content, improving code suggestions and allowing automation of repetitive dev tasks.
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JetBrains IDE MCP
Integrates with JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.) to let an AI agent navigate and edit code within the editor. The MCP plugin exposes editor functions so the AI can open files, highlight code, and suggest changes in real-time. Perfect for debugging and code generation assistance directly in the IDE – e.g. explaining errors, generating functions, or refactoring code on the fly. Enhances developer productivity by bringing AI help into the coding workspace.
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Riza
A platform for arbitrary code execution and tool use by LLMs. Riza allows an AI to run code (in a sandboxed environment) and invoke various developer tools or CLI commands as needed. Ideal for complex coding tasks where the AI needs to compile/run code, test outputs, or chain multiple tool invocations. Greatly extends an AI's capabilities in software development – the model can not only write code but also execute and verify it.
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Terminal and Editor Integrations
Terminal-Control enables secure execution of shell commands, directory navigation, and file operations via an AI. Similarly, Neovim MCP connects to a Neovim editor session to let the AI read/edit text in an editor environment. By allowing controlled terminal access, these MCPs let the AI handle devops chores or repetitive tasks (like running tests, listing files) hands-free, reducing context switching for developers.
Gaming Development MCPs
Unreal Engine Code Analyzer
Provides deep analysis of Unreal Engine C++ codebases, allowing an AI to understand classes, inheritance hierarchies, and references in the game code. It can search through Unreal's source, map class relationships, and identify patterns. Speeds up troubleshooting and onboarding in Unreal projects by offloading code comprehension to AI.
Game Data API MCPs
Exposes game analytics data via AI. The OpenDota MCP, for example, lets an AI retrieve Dota 2 match data, player statistics, and other game records through the OpenDota API. Enables data-driven game development by allowing AI to effortlessly compile and summarize large volumes of gameplay data.
NPC Dialogue & World-Building Assistants
These would connect AI language models to game engines for dynamic NPC behavior and world editing. For example, an MCP could expose a game's scripting API, so the AI can update an NPC's dialogue tree or modify world objects in real-time. Significantly speeds up content creation and makes game worlds more immersive.
A developer could use an MCP to link an AI to their game's console commands for physics parameters. The AI might adjust gravity or NPC aggression on the fly during playtesting. This real-time tuning via AI can help find fun configurations faster than manual trial-and-error.
Web Scraping & Data Extraction MCPs
Apify Actors MCP
Leverages Apify's platform of 3,000+ pre-built web scraping and automation actors to extract data from websites, e-commerce stores, social media, search engines, maps, etc. An AI can invoke specific Apify actors (like a Google Maps scraper or an Amazon product crawler) via this MCP. Scalability – complex scraping jobs run in Apify's cloud, returning structured data without the AI needing to handle low-level details.
Browser Automation MCPs
Browserbase automates a real browser in the cloud – enabling web navigation, clicking, form filling, and more, all controlled by the AI. Puppeteer MCP similarly gives the AI a headless Chrome browser to load pages, execute JavaScript, and even take screenshots. Provides human-like browsing capabilities to the AI.
Specialized Web Scrapers
Firecrawl offers advanced scraping with headless browser rendering, PDF support, and smart rate limiting. Oxylabs MCP taps into Oxylabs' Web Unblocker API for scraping at scale with IP rotation and HTML parsing for structured data. Robustness and reliability in web data gathering – these MCPs handle anti-scraping measures and heavy content.
"Fetch" and Search MCPs
Lightweight content fetchers and search tools. Fetch simply retrieves a URL's content (HTML, JSON, text) and can convert it to a format suitable for LLM consumption. Brave Search MCP queries the web via Brave's search API and returns results. Adds general knowledge and current data to an AI's toolkit.
Blockchain & Web3 MCPs
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Bankless Onchain MCP
Provides natural language querying of on-chain Ethereum data. An AI can retrieve token balances, transaction histories, smart contract states, and more via this server. Turns complex blockchain queries into simple questions for AI. Developers and analysts save time as the AI translates high-level prompts into precise blockchain data fetching, making on-chain analytics more accessible.
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EVM Network MCP
A comprehensive server covering 30+ EVM-compatible blockchains (Ethereum, Polygon, BSC, etc.). It supports reading token balances (native coins, ERC-20s, NFTs), decoding smart contract calls, tracking transactions across multiple chains, and even ENS domain resolution. Broad coverage – one MCP handles many networks, so integration is simplified.
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GOAT (On-Chain Actions)
GOAT stands for a toolkit that enables 200+ on-chain actions across major networks. That includes not just reading data but potentially writing transactions: an AI could initiate token transfers, interact with DeFi protocols, or deploy contracts (presumably with proper security and user confirmation). Adds transactional capabilities to AI – moving from just analysis to action.
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Other Web3 MCPs
The Solana Agent Kit MCP connects AI to the Solana blockchain, supporting dozens of protocol actions (DeFi, NFTs, etc.). The Ergo Blockchain MCP targets the Ergo blockchain, enabling balance checks, transaction tracing, and forensic analysis of addresses. Domain-specific insights leading to more effective analysis and accurate information.
AI Workflow & Automation MCPs
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Task Manager MCP
Provides a queue-based system for managing tasks via AI. An AI (like Claude Desktop) can push tasks into a queue, track their status, and execute them one by one using this server. Enforces structured, sequential execution which is crucial for AI "agent" systems that need to remember to do step A then B.
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Knowledge Graph Memory
A persistent memory MCP that stores information in a knowledge graph. It lets the AI save facts or context between sessions and retrieve them later as needed. Solves the context limit problem by offloading memory to an external store. The AI can recall details that exceed its prompt window, leading to more consistent and contextually rich performance over time.
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Personal Intelligence Framework (PIF)
A high-level framework MCP providing file operations, structured reasoning tools, and a journaling system. It's designed to support continuous collaboration between human and AI by keeping a "journal" of interactions and reasoning steps. Encourages reflective AI behavior by giving it a place to record its chain-of-thought and refer back to it.
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Integration & Orchestration MCPs
UnifAI Network MCP allows an AI to dynamically discover and invoke other tools/services, effectively acting as a gateway to many capabilities. Integration App MCP connects to various SaaS applications on behalf of a user (like an integration platform). Simplifies complex integrations by presenting one interface to the AI.
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Sequential Reasoning Tools
Sequential Thinking MCP helps an AI break down problems into steps and iterate solutions. More effective problem-solving as the AI can "think out loud" and refine its plan. This kind of MCP enhances the workflow quality – ensuring the AI's automation is logically sound, not just fast.
Enterprise & Business Process MCPs
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Atlassian (Jira/Confluence) MCP
Allows AI to search and read Confluence pages, query Jira issues, and access project metadata from Atlassian Cloud.
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Salesforce MCP
Connects to Salesforce CRM to retrieve and manipulate data like contacts, leads, opportunities, and even metadata.
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ServiceNow MCP
Interfaces with ServiceNow IT service management, allowing the AI to create or update tickets, query incident histories.
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Snowflake (Data Warehouse) MCP
Enables secure queries to a Snowflake data warehouse from an AI. The AI can inspect schemas and run read-only SQL queries.
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HubSpot MCP
Integrates with HubSpot's CRM, letting the AI manage contacts, companies, and deals – including creating new entries.
Enterprise MCPs help manage large-scale business workflows, from financial analysis to CRM updates, ERP tasks, and knowledge management. They connect AI agents with corporate data and systems (often via APIs of popular enterprise software). These MCPs save time in knowledge retrieval by letting the AI act as an intranet search engine, instantly finding relevant project info across wikis and issue trackers. This can enhance decision-making and reduce the need for employees to manually dig through systems.
Other notable enterprise MCPs include ones for e-signatures (automating contract workflows), ERP systems (though not explicitly listed, MCP could hook into SAP/Oracle via APIs), BI tools like Lightdash for analytics, and collaboration platforms like Notion or Monday.com for coordinating tasks.
Cybersecurity & Threat Detection MCPs
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Integrated Security Response
Combining multiple security MCPs for comprehensive protection
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Keycloak MCP
Manages identity and access control systems
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Descope MCP
Monitors authentication events and user activity
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Shodan MCP
Scans for exposed devices and vulnerabilities
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OpenCTI MCP
Retrieves threat intelligence information
Cybersecurity MCPs enhance AI-driven security operations, from vulnerability scanning to threat intelligence retrieval and incident response. They give AI tools the ability to query security databases and systems to detect and address threats proactively.
The OpenCTI MCP connects to an OpenCTI platform (an open-source threat intelligence system), enabling the AI to fetch information on threat actors, malware, attack techniques, and intelligence reports. It augments security teams by quickly delivering relevant threat intel within an analyst's chat.
The Shodan MCP queries the Shodan API, which is a search engine for internet-connected devices and known vulnerabilities. This MCP lets the AI find open ports, services, and CVE info associated with IPs or domains, providing real-time security awareness.
In addition to these, general database MCPs (SQLite, MySQL, MongoDB, etc.) can be used for security data as well – e.g., an AI could query a logging database for suspicious entries. The MCP framework's controlled tool use is especially crucial in security to prevent an AI from causing damage while it helps defend systems.
Creative Content & Media MCPs
EverArt (Image Generation)
Interfaces with multiple image generation models to create artwork from text prompts. The AI can request images (in various styles or using different underlying generative models) and get back image URLs or data. Enables rapid visual prototyping – users can get custom images in seconds by leveraging AI, speeding up the creative iteration process.
Generates images or even short videos based on templates designed in Placid.app. The AI fills in text, colors, or other template variables to produce things like banners, social media visuals, or simple animated videos. Consistent, fast content creation for non-designers who can have the AI generate professional-looking graphics that adhere to a style guide.
ElevenLabs (Text-to-Speech)
Connects to the ElevenLabs API to produce realistic voiceovers from text. The AI can generate speech in multiple voices or even emulate a specific voice if a model exists. High-quality audio generation on demand, which saves the cost and time of traditional voice recording.
Video Editor MCP
Connects to a video editing service (Video Jungle API) to add or edit video content. The AI can search a video library, trim clips, or assemble a timeline. Dramatically speeds up video editing tasks by offloading them to AI.
Creative MCPs span a broad range – from assisting writers (there are MCPs for Ghost CMS or Notion that help with content management) to helping artists code generative art. The common theme is empowering creators to produce more and experiment freely, with AI handling grunt work or offering suggestions. The result can be a significant boost in productivity and innovation in any media domain.
Top Overall MCPs (Highest Utility & Adoption)
Sequential Thinking MCP
A reasoning booster that helps an AI break complex problems into steps and reflect on solutions. It's widely used (5,500+ uses) because it improves any task's outcome by structuring the AI's thought process. It's especially helpful in coding, planning, or analytical domains where step-by-step reasoning is key.
"WCGW" Shell & Coding Agent
("What Could Go Wrong") – A powerful dev agent. This MCP (around 4,900 uses) combines shell access and coding abilities for an AI, allowing it to run commands and code. It's popular for automating development workflows and testing code within an AI conversation, but it requires careful use due to its wide access.
GitHub MCP
Code repository integration. With nearly 2,900 uses, it's one of the most popular MCPs. By letting AIs interface with GitHub, it's invaluable for any coding assistant (enabling code search, edits, pull request analysis, etc.). Its high utility across many projects makes it a go-to choice.
Brave Search MCP
Web search at your fingertips. This server (680+ uses) brings web and local search into AI chats. It's highly effective for keeping the AI informed with up-to-date information or verifying facts. Many users include a search MCP by default to overcome the knowledge cutoff of base models.
Web Research MCP
Research assistant. With 533 uses, this MCP augments an AI's research skills by integrating Google Search, webpage content extraction, and session tracking. It's essentially a specialized web browser for the AI, great for academic or market research tasks where multiple sources need to be read and summarized.
With dozens of MCP servers available, a few stand out for their broad usefulness, effectiveness, and community adoption. Other notable MCPs include iTerm (Terminal) MCP (400+ uses), TaskManager MCP (374 uses), SQLite Database MCP (274 uses), Fetch MCP (269 uses), Knowledge Graph Memory MCP (263 uses), and Playwright Browser MCP (257 uses).
The "top" MCPs combine general usefulness (applicable to many scenarios) and high effectiveness (they do their job well). Tools like repository access, web browsing, memory, and task management are broadly useful, which is why they rank highly.
MCP Selection & Management Cheat Sheet
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Optimize for Utility
Balance multi-purpose and specialized MCPs
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Maintain and Update
Keep track of MCP ecosystem changes
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Test and Monitor
Validate performance and resource usage
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Configure Securely
Set up proper permissions and API keys
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Select Strategically
Choose MCPs aligned with project needs
Choosing and using MCPs wisely will ensure you get the most out of your AI-augmented workflow without overwhelm. Below is a cheat sheet of best practices and tips for integrating MCPs into your projects:
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Align with Project Needs
List out what tasks or data your project requires. Pick MCPs that match those requirements directly. For example, if your project involves a lot of web research, include a search and a web-scraping MCP. If it's code-heavy, prioritize GitHub, code execution, and debugging MCPs. Avoid "just in case" additions – each active MCP should have a clear purpose to conserve your 40-slot budget.
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Mind the 40 MCP Limit
Less is more. You can have up to 40 MCP servers active, but managing all of them can become complex. Aim to integrate the fewest necessary MCPs to accomplish your goals. This reduces overhead and potential tool conflicts. If you reach the limit, evaluate which MCPs overlap in functionality and disable ones you use rarely.
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Integration into Workflows
Incorporate MCP usage into your AI's prompts or chain-of-thought. Best practice is to clearly indicate when the AI should use a tool (some clients use special syntax like /mcp ). For instance, "/mcp search: 'query'" could be in the prompt when the AI needs to look something up. By guiding the AI when to invoke an MCP, you ensure it doesn't hallucinate tool usage.
Security Best Practices for MCPs
Only use community MCPs from trusted sources
Especially if they'll handle sensitive data. Review their code or documentation for any potential risks. Never expose private data (API keys, personal info) to an MCP unless necessary and you understand how that data is handled.
Configure proper access controls
If running an MCP server locally, be aware of what it can access. For example, a Filesystem MCP should be configured to only allow reading a specific directory to avoid exposing your entire drive.
Use sandboxed environments
For code or shell MCPs, use sandboxed versions (like code-sandbox-mcp) or limit their permissions to prevent accidental damage. Always double-check the access levels you're granting; for instance, a database MCP should ideally be read-only unless you trust the AI to make changes.
Checklist Before Activation
Relevance: Does this MCP directly help achieve a task in my workflow?
Configuration: Have I provided the necessary keys/credentials? Tested a basic call?
Safety: Am I using the safest variant of this MCP (e.g., sandboxed, read-only mode)?
Overlaps: Do I have another MCP that does something similar? Can one tool do double-duty to save a slot?
Understanding: Does the AI know how to use this MCP correctly? (If not, provide a few example prompts or adjust instructions.)
By following this guide and cheat sheet, you can confidently select the right Model Context Protocol servers for your needs and integrate them for maximum benefit. The result is an AI development pipeline turbocharged with specialized skills – from writing better code and scraping web data, to handling business processes and creating media – all working in concert. Just remember to plan, start small, and scale up the MCP arsenal as you validate each addition. Happy building with MCPs!