Top 10 MCP Servers & Tools to Watch in 2025

The AI revolution is already here and it’s going faster than you think. As intelligent agents gain capabilities and become more tightly integrated with real-world workflows, the need for more robust, scalable infrastructure has never been greater. The challenge of this evolution is primarily driven by the Model Context Protocol (MCP) a protocol for building AI systems that can remember things about how they are configured, know things about their context, engage with tools, and take action across applications safely and reliably.

In 2025, MCP servers and tools will define the architecture around how we will build and manage context enabled AI agents, and scale them. Whether you’re deploying assistants that automate workflows, manage code, or run your business, being aware of the leading tools available in the MCP ecosystem will allow you to build a better future proof stack while staying competitive in a fast CLI to AI world.

Introduction: The Rapidly Growing MCP Ecosystem

Advancing from prompt based LLMs into persistent, interactive AI agents will require more than just intelligent models it’ll require intelligent infrastructure. Model Context Protocol (MCP) allows for agents to properly span the gap between language models and real world work or tasks. Using Protocols allows agents to hold context over time, interact in a secure way with external tools, and manage fluid and dynamic workflows.

MCP servers allow agents to not only reason in comfortable structures over one input but also integrate and interact deeply with operational systems and plan to take action in continuity of interaction and with situational awareness. In 2025, MCP is not experimental anymore   it will be foundational to the further proliferation of agents.

In this guide, we outline the 10 most prominent and prolific MCP servers and tools that will shape and define the future of agent based AI. We present their key features, use cases, and strategic advantages.

How We Ranked the Tools

Our choice and rating of MCP servers and tools is the result of a mixture of their technological merit, how widely used they are, and how mature the ecosystem is. The criteria include:

• Adoption and Community Support
• Security and Enterprise Readiness
• Integration Capabilities
• Documentation and Ease of Use
• Scalability and Customisation Options

Now, let’s review the tools that are redefining how intelligent systems interact with context and the world around them.

Top 10 MCP Servers & Tools

GitHub MCP Server

Description: Allows AI agents to track pull requests, issues, code reviews, and CI/CD pipelines.
Use Case: A particularly good solution for developer focused AI assistants that are automating engineering workflows.
Why It Matters: The ability to seamlessly integrate with your GitHub repositories means your AI is able to automate relevant tasks in a context aware way when it comes to software development. GitHub brings traceability and assurance to AI agents interacting with code.

Slack MCP Server

Description: Connects intelligent agents with team messaging environments in real time.
Use Case: Can provide collaborative AI assistants, chat summarizers, and task managers in messaging environments, enabling ‘Always On’ team coordination and collaboration.
Notable Features: supports channel level context, file uploads, user mentions, and threading. An ideal platform for team collaboration with AI agents understanding the flow of the dialogue.

PostgreSQL MCP Server

Description: Connects agents to structured data in SQL databases, with the ability to receive transactional data and provide responses back to systems.
Use Case: A wide variety of advanced enterprise application use cases, including traditional analytics, CRM access, processing of business logic and time-based workflows, and, more importantly, managing audit trailing of events from structured data.
Highlight: Offers strong schema validation, permission management, and scalable performance for interacting structured data with transactional updates.

Community MongoDB MCP Server

Description: A NoSQL integration layer built for flexible document style data structures.
Use Case: Supports dynamic user sessions, contextual memory, chat histories, and flexibility with content.
Community Status: Actively developed with mature open schema mapping capabilities and support for JSON like structures, it is well suited to handling unstructured data.

Community Notion MCP Server

Description: Elevate Notion as a contextual, real time, intelligent knowledge base for AI agents.
Use Case: Agents will be able to track projects, document decisions, and summarize content from nested workspaces.
Benefits: The community version supports real-time tracking of updates, relationships across multiple pages, and a highly intuitive inner window and content structure  making it ideal for internal AI knowledge assistants.

Community Jira MCP Server

Description: Integrates intelligent agents side by side in agile project management environments.
Use Case: Help the agile team with sprint planning, ticket triaging, and productivity tracking.
Edge: Sanity will sync directly with Jira boards to provide user context aware recommendations and backlog grooming support for the dev team.

Official Python MCP Library

Description: A fully featured SDK for building and managing MCP servers natively in Python.
Strength: Actively maintained by the core team, which has enterprise grade support, rich documentation, and highly impactful integration.
Use Case: Ideal for developers implementing RAG (retrieval augmented generation) systems and associated orchestration logic, or building tailored intelligent agents.

Community TypeScript/JS SDK

Description: Lightweight SDK for developing MCP compatible tools and agents from the web or Node.js.
Use Case: Facilitates developing web native agents, API connectors, and enhanced automation in the front-end.
Advantage: Fast to prototype, flexible for hybrid environments, and supported by a growing open-source community.

MCP CLI Tool

Description: A command line utility for debugging, inspecting, and interacting with MCP servers.
Use Case: For DevOps teams to aid server discovery, mock context injection, and live log monitoring.
Features: Provides live inspection, mock requests, and log monitoring; improves visibility and control during agent orchestration.

Filesystem MCP Server

Description: An experimental tool that provides the ability for agents to interact with the local filesystem securely.
Use Case: For use with log analysis, summarization tasks, file management, or AI-powered searches in the codebase.
Current Status: Community maintained with basic read/write/search support; limited enforcement of security controls, and is best for internal or research projects.

Emerging Tools

The MCP ecosystem is rapidly evolving. Here are some noteworthy tools in beta or early release:

Google Drive MCP Server

Enables AI agents to navigate, summarize, and organize cloud stored files. Great for work in collaborative environments and documentation workflows.

AWS S3 MCP Connector

Facilitates access to large scale object storage for AI agents, for actions such as classification, analysis, and archival.

Calendar MCP Server

Allows agents to manage scheduling, sending invites, and syncing available times essentially making autonomous virtual assistants time aware.

These are examples of MCP’s expanding reach into cloud infrastructure and productivity ecosystems.

How to Get Involved in the MCP Community?

This emergence of MCP is not happening in a vacuum it consists of an active, engaged developer community of passionate people.

Ways to engage:

• Contribute to open-source MCP servers and SDKs
• Create plugins or extensions for your favorite tool
• Participate in discussions on orchestration forums and GitHub
• Post your use cases, integrations, and learnings

Your contribution also contributes to defining the standards and best practices for intelligent agent development.

Why is NextGenSoft at the Forefront of MCP Tooling?

NextGenSoft does not just adopt MCP we are defining it.

Our expertise spans:

• Building custom MCP servers for enterprise requirements
• Security first infrastructure for AI agents
• Full stack orchestration development frameworks for LLMs
• Context aware memory and planning logic
• Cloud native integrations for SaaS, APIs, and databases

Whether you are building your first AI agent or scaling to production, NextGenSoft offers best fit strategies and development support to help design your AI systems to be more intelligent, secure, and actionable.

Conclusion: The Future of AI Workflows Is Context Aware

AI is on a gradual transition from non-contextual single-response behavior to real time multi turn decisions in a rich context. In this new age of agents, the importance of high-performance, extensible, and secure MCP servers as well as advanced development and collaboration tools for those servers cannot be overstated.

You can already see the true nature of the Model Context Protocol in action from GitHub and Notion to experimental file systems and calendar managers, the MCP is being rapidly adopted as the backbone for the AI infrastructure. The developers and organizations that slowly adopt the right tools today will become tomorrow’s leading intelligent applications.

Partner With NextGenSoft To Scale MCP Adoption In Your Stack!

If you’re ready to start building smarter AI workflows with the added security provided by the MCP, NextGenSoft is your trusted partner.

From helping with your infrastructure, orchestration, or integration needs to providing end-to-end support for all aspects of building contextually aware AI agents and scaling them.

Let’s be part of the future of intelligent systems. Let’s connect, talk with the NextGenSoft team today.