The Ultimate Guide to MCP Servers: Best Options for Building AI-Ready Apps

The Model Context Protocol (MCP) has emerged as a pivotal standard, enabling seamless integration between AI models and external tools or data sources. Selecting the best MCP servers is essential for developers aiming to build intelligent, context-aware AI applications.

This comprehensive guide goes beyond a mere list of options. It delves into the evaluation criteria essential for choosing the right MCP server, explores practical use cases, and offers deployment tips to help you make informed decisions. 

Whether you're a developer, data engineer, or AI enthusiast, reading this guide on MCP servers will empower you to build more effective AI-ready applications.

What Makes a Great MCP Server in 2025?

As AI applications become increasingly sophisticated, the Model Context Protocol (MCP) has emerged as a pivotal standard for integrating large language models (LLMs) with external tools and data sources.

Selecting the right MCP server is crucial for building intelligent, context-aware systems. Here are the key factors to consider:

1. Performance & Latency

• Low Latency: MCP servers should offer minimal response times to ensure real-time interactions. Implementations using edge computing, such as Cloudflare Workers, can achieve sub-50 ms cold starts, enhancing performance.
• Efficient Context Handling: Efficient management of context windows is essential for multi-turn conversations and task chains, reducing overhead and improving responsiveness.

2. Hosting Model: Open-Source vs. Managed

• Open-Source: Provides flexibility and control, allowing developers to customize and optimize the server according to specific needs. Projects like the community-driven ONES (modelcontextprotocol/servers) offer reference implementations in TypeScript and Python.
• Managed Hosting: Platforms such as Back4app offer turnkey MCP hosting with built-in scaling, monitoring, and support, reducing the operational burden.

3. Extensibility and Plugin Support

• Adapter Libraries: Tools like LangChain’s MCP Adapters simplify connecting to multiple MCP servers within LangGraph agents, facilitating integration.
• SDKs: Vercel’s AI SDK introduces experimental features like createMCPClient for seamless tool retrieval and integration with various LLMs.

4. Context Retention Capabilities

• Stateful Context: A robust MCP server should maintain context across sessions, which is crucial for applications that require memory of past interactions.
• Sharding & Archiving: Advanced servers should implement strategies to manage context data efficiently, such as sharding older context to secondary storage while keeping active context readily accessible.

5. Security, Authentication, and Sandboxing

• Authentication Protocols: Implementing OAuth 2.1 flows ensures secure access controls, preventing unauthorized interactions.
• Sandboxing: Running tools in isolated environments, such as containers or WebAssembly (Wasm), prevents potential security breaches and ensures safe execution.

6. Compatibility with Major LLMs

• Broad Compatibility: The server should support integration with major LLMs like OpenAI, Claude, and Mistral, ensuring versatility and adaptability to various AI models.
• Community Connectors: Utilizing community-developed connectors can extend support to a wide range of inference backends, enhancing the server's utility.

MCP vs. Serverless APIs: Complementary or Competitive?

In AI application development, Model Context Protocol (MCP) and Serverless APIs offer unique advantages. While MCP provides structured and evolving context to AI models, Serverless APIs offer event-driven, scalable execution. Rather than being competitive, these two architectures are often complementary, each addressing different aspects of AI application needs.

Before we dive into the comparison further, let’s set the base right. For a broader perspective on how MCP stacks up against classic REST-based systems, check out this detailed comparison between MCP and traditional APIs. 

Here is the shorter explanation of each term:

• Model Context Protocol (MCP): MCP is designed to supply AI models with structured, relevant, and evolving context, which is crucial for real-time, secure, and performant AI behavior. It enables AI applications to maintain state across interactions, facilitating more coherent and contextually aware responses.
• Serverless APIs: These are application programming interfaces that run on serverless computing platforms, such as AWS Lambda, Azure Functions, or Google Cloud Functions. They allow developers to deploy code that automatically scales and executes in response to events, eliminating the need to manage the underlying server infrastructure.

Now that you know what each term means, let’s dive further into their differences: 

Key Differences Between MCP and Serverless APIs

How They Complement Each Other

In practice, MCP and Serverless APIs can be integrated to leverage the strengths of both architectures:

• Event-Driven Context Updates: Serverless functions can handle events requiring context updates in AI applications. For instance, when a new document is uploaded, a serverless function can process this event and update the relevant context in the MCP server, ensuring that the AI model has access to the most recent information.
• Asynchronous Task Handling: While MCP excels at maintaining real-time, context-rich interactions, certain tasks, like data preprocessing or long-running computations, are better suited for asynchronous execution. Serverless functions can handle these tasks independently and update the MCP server upon completion, allowing AI applications to remain responsive.
• Scalable Integration with External Services: Integrating external services, such as third-party APIs or databases, can be efficiently managed using serverless functions. These functions can act as intermediaries, fetching data from external sources and formatting it appropriately before updating the MCP server.
• Modular and Maintainable Architecture: Combining MCP with Serverless APIs promotes a modular architecture where each component has a well-defined role. MCP focuses on maintaining context and facilitating AI interactions, while serverless functions handle discrete tasks and integrations. This separation enhances maintainability.
• Cost-Effective Scaling: Serverless architectures offer cost-effective scaling by charging only for actual usage. Organizations can optimize resource utilization and reduce operational costs by offloading specific tasks from MCP to serverless functions.

Want a side-by-side breakdown of when to use MCP and when to use Serverless APIs? This post explores their unique strengths and when to use each.

Top MCP Servers You Should Know About in 2025

Now that you know the top MCP servers, you might also want to explore the best AI APIs available today to integrate with your stack and enhance your application’s capabilities.

Building Your MCP Server

In this section, we’ll learn how to construct a custom MCP server from scratch. From choosing a framework, installing the MCP SDK, defining protocol endpoints, registering tools, adding middleware, securing your server, and deploying to production. By the end, you’ll have a boilerplate you can extend for any AI-powered application.

Choosing a Framework

Depending on your stack and preferences, you can build an MCP server in Python or JavaScript/TypeScript.

• FastAPI (Python) is a popular choice, with the fastapi_mcp package providing zero-config integration for existing FastAPI apps.
• Gradio (Python) allows you to turn any Python function into an MCP tool by simply setting mcp_server=True when launching your demo.
• Express (Node.js/TypeScript) can be paired with the official TypeScript MCP SDK to expose endpoints as MCP tools, giving you complete control over routes and middleware.

Installing the MCP SDK

First, install the SDK that matches your chosen framework:

# Python MCP SDK
pip install modelcontextprotocol

The official Python MCP SDK lets you build servers exposing data and functionality to LLMs in a standardized way.

# TypeScript MCP SDK
npm install @modelcontextprotocol/typescript-sdk

The TypeScript SDK provides helper classes to define tools, capabilities, and request handlers for MCP servers.

Defining Protocol Endpoints

Every MCP server needs at least three HTTP routes:

• /capabilities: Lists available tools and their schemas.
• /run: Invokes a specified tool with inputs.
• /health: Simple healthcheck for uptime monitoring.

Here’s a minimal FastAPI example based on Anthropic’s quickstart guide:

from fastapi import FastAPI, Request
from modelcontextprotocol.server import MCPServer

app = FastAPI()
mcp = MCPServer()

@app.get("/capabilities")
async def capabilities():
    return mcp.list_tools()

@app.post("/run")
async def run(request: Request):
    payload = await request.json()
    result = await mcp.invoke_tool(payload["tool"], payload["inputs"])
    return {"output": result}

@app.get("/health")
async def health():
    return {"status": "ok"}

These routes follow the MCP specification for server implementations.

Registering Tool Functions

Tools are simply functions you register with your MCP server:

# Continuing from the FastAPI example above
@mcp.tool(name="count_letters", description="Count letters in a string")
def count_letters(text: str) -> int:
    return len(text)

With the Python SDK, decorate any function and it becomes an MCP tool accessible via /run.

In Gradio, you can expose your interface directly:

import gradio as gr

def summarize(text: str) -> str:
    # your summarization logic here
    return text[:100] + "..."

demo = gr.Interface(fn=summarize, inputs="text", outputs="text")
demo.launch(mcp_server=True)

Launching with mcp_server=True automatically adds /capabilities and /run for you.

Middleware Patterns

Enhance your server with middleware for everyday concerns:

• Rate Limiting: Use libraries like fastapi-limiter or koa-ratelimit to protect against abuse.
• Logging & Telemetry: Integrate with Prometheus or OpenTelemetry to track tool usage and performance.
• CORS & Compression: Apply FastAPI’s CORSMiddleware and GZipMiddleware for broader compatibility and performance.

Security & Authentication

Protect your MCP server with industry-standard controls:

• OAuth 2.1 / API Keys: Restrict access to /run based on tokens—use frameworks like fastapi-security.
• Sandboxing: Run untrusted code or third-party integrations in isolated environments (e.g., Docker containers or WebAssembly).

Deployment Options

Choose a hosting platform that matches your latency, scalability, and cost requirements:

• Fly.io / Docker: Run your MCP server in a global Docker cluster for low-latency, high-availability deployments.
• Cloudflare Workers: Deploy edge-native MCP servers to minimize round-trip times, especially for globally distributed users.
• Kubernetes: For enterprise scale, containerize your server and use Helm charts to manage releases.

Testing & Observability

• Local Testing: Use the official MCP client in the Python or TypeScript SDKs to write integration tests against your /capabilities and /run endpoints.
• Monitoring: Integrate Treblle (or similar API observability tools) to gain real-time insights into request volumes, latencies, and errors. Integrating Treblle in your application takes less than five minutes and a few lines of code. 

MCP Trends to Watch In 2025

The Model Context Protocol (MCP) has rapidly transitioned from a novel concept to a foundational element in AI infrastructure. As we look ahead, several emerging trends are poised to shape its evolution, enhancing the capabilities and integration of AI systems across various domains.

1. Standardization and Broad Adoption

As MCP solidifies its position as a universal interface, the volume of tool and model integrations will grow rapidly. Without clear standards and disciplined development practices, this can introduce API debt, complicating upgrades and increasing fragility across AI workflows. To understand how this kind of technical debt emerges—and how to avoid it—read more on why API debt is the new technical debt.

2. Dynamic Tool Discovery and Auto-Synchronization

Emerging frameworks like ScaleMCP are introducing dynamic tool selection and auto-synchronization capabilities. These advancements allow AI agents to autonomously discover, integrate, and update tools during runtime, enhancing flexibility and reducing manual intervention. 

3. Advancements in Multi-Agent Coordination

MCP is facilitating more sophisticated multi-agent systems by providing standardized context-sharing mechanisms. This enables coordinated interactions among specialized agents, improving efficiency in complex tasks such as collaborative research and enterprise knowledge management. 

4. Integration with Domain-Specific Expert Systems

Innovations like the MCP-based Internet of Experts (IoX) are equipping AI models with domain-specific expertise. By integrating lightweight expert models through MCP, AI systems can access specialized knowledge without extensive retraining, enhancing performance in areas like wireless communications. 

5. Enhanced Security and Governance

As MCP becomes more integral to AI operations, ensuring robust security and governance is paramount. Ongoing research is addressing potential vulnerabilities, such as prompt injection and unauthorized tool access to establish best practices for secure MCP implementations. For more on how these risks are being tackled in real-world deployments, this article explores the evolving security model around MCP and AI systems. 

6. Expansion into Diverse Industries

Beyond traditional tech sectors, MCP is making inroads into industries like finance, healthcare, and manufacturing. Its ability to standardize AI interactions with various tools and data sources is proving valuable for automating complex workflows and enhancing decision-making processes.

7. Development of Advanced Tool Registries

Efforts are underway to create comprehensive registries of MCP-compatible tools and services. These registries aim to streamline the discovery and integration of tools, fostering a more cohesive and efficient AI ecosystem.

As we conclude this comprehensive guide on Model Context Protocol (MCP) servers, it's evident that selecting the right MCP server is pivotal for developing AI-ready applications that are context-aware, scalable, and secure. Whether you opt for a managed solution or build your own, it's essential to consider factors like performance, extensibility, and integration capabilities.

In the dynamic landscape of AI application development, maintaining observability becomes crucial. Tools like Treblle offer real-time API monitoring, comprehensive logging, and insightful analytics to help developers quickly identify and resolve issues, ensuring smooth API operations. 

By combining the right MCP server with robust observability practices, you're well-equipped to build and maintain AI applications that are both powerful and dependable.