Best MCP Servers for Software Developers and Engineers

AI assistants are getting smarter, but most of them still cannot directly act in real systems. They can explain or suggest, but they cannot execute it. MCP servers solve that gap.

MCP servers give AI a safe way to call real tools, APIs, workflows, and systems. In this blog post, we will explore the MCP servers that are actually helping developers, SREs, and automation engineers.

What is MCP Server?

MCP server is a small service that exposes actions, resources, or queries to AI using the Model Context Protocol. MCP is an open standard that defines how AI agents can connect to external systems in a consistent and permission-controlled way. It is the "bridge" between AI and real infrastructure.

In theory, an MCP server can be built for any platform. The building blocks include connection lifecycle, schema definition, authorization, resource enumeration, streaming data, clear error design, and mapping real system capabilities into MCP actions. In practice, it is not trivial.

Teams must think about versioning, capability boundaries, idempotent behaviors, safe scoping of high-impact operations, and returning meaningful errors that AI models can interpret reliably. Building an MCP server takes time and careful design. Using existing MCP servers can significantly speed up development and let the focus directly on value.

List of Best MCP Servers for Software Engineers

The software developer team at Improving stays at the forefront of innovation, constantly testing and refining the latest tools, frameworks, and protocols shaping the AI ecosystem. Our software engineers actively explore how MCP servers can bridge AI systems with real-world platforms and workflows, making automation and integration more seamless. Based on hands-on testing and real project experience, here is a curated list of MCP servers our experts recommend.

DevOps & Infrastructure Management Servers

• Kubernetes MCP Server: Allows AI assistants to connect with Kubernetes/OpenShift clusters to perform CRUD operations, manage pods, deployments, services, and logs. Flux159's mcp-server-kubernetes also connects to existing kubectl contexts.
• GitHub MCP Server: Facilitates automating and managing GitHub repositories, issues, pull requests (PRs), branches, and releases via AI agents.
• AWS MCP Server: Enables AI assistants to manage AWS resources such as S3, DynamoDB, VPC configurations, EC2, and IAM.
• Azure DevOps MCP Server: Integrates with Azure DevOps for managing work items, pipelines, repositories, and pull requests.
• Terraform MCP Server: Integrates with the Terraform ecosystem for Infrastructure as Code (IaC) development.
• Jenkins MCP Server: Enables LLMs to interact with Jenkins for listing jobs, triggering builds, and retrieving logs.
• Argo CD MCP Server: Allows AI assistants to interact with Argo CD deployments and applications using natural language commands.
• Docker Hub MCP Server: Connects Docker Hub APIs to LLMs for intelligent image discovery and repository management.
• Cyclops MCP Server: Enables AI agents to manage Kubernetes resources through the Cyclops abstraction layer.
• Alibaba Cloud MCP Server: Official server for managing Alibaba Cloud resources including ECS instances and Cloud Monitor metrics.

Testing and Validation Servers

• Postman MCP Server: Curated catalog of MCP servers for interacting with external services via defined endpoints.
• Testkube MCP Server: Enables AI assistants to interact with testing workflows, executions, and artifacts on Testkube.
• Playwright MCP Server: Official Microsoft implementation for browser automation through structured accessibility snapshots.

Monitoring & Observability Servers

• Prometheus MCP Server: Enables LLMs to run PromQL queries and analyze Prometheus metrics.
• Grafana MCP Server: Allows programmatic interaction with Grafana dashboards and data sources.
• Datadog MCP Server: Enables operations like retrieving monitors, logs, metrics, and incidents.
• Comet Opik MCP: Natural language exploration of LLM observability data and monitoring metrics.
• Influx DB MCP Server: Official server for InfluxDB time-series data management.
• Hydrolix MCP: Time-series datalake schema exploration and query capabilities.

Daily Task Automation & Productivity Servers

• Slack MCP Server: Connects AI models to Slack for channel management and messaging.
• Filesystem MCP Server: Secure file system activities including read/write and directory management.
• Notion MCP Server: Bridge between AI agents and Notion workspace for pages and databases.

Database & Other Specific Use Cases

• ClickHouse MCP Server: Safe query execution and read-only enforcement for ClickHouse.
• MCP Server Replicate: Python-based MCP for AI model inference and image generation.
• La-rebellion MCP Server: TypeScript implementation with simplified facade pattern.
• Chroma MCP Server: Access to local and cloud Chroma vector database instances.
• Couchbase MCP Server: Manage Couchbase Capella and self-managed clusters.
• Redis MCP: Official Redis implementation for key-value operations and search.
• Snowflake MCP Server: Official server with full RBAC and comprehensive authentication support.

Security Domain

• MCP-Scan: Scans MCP servers for vulnerabilities like prompt-injection and over-permissive tools.
• MCP Gateway: Security proxy with reputation scoring and real-time risk alerts.
• MCP for Security: Exposes security tools like Nmap and SQLMap to AI assistants.
• Contrast MCP Server: Automated vulnerability detection and AI-guided remediation.
• Panther MCP Server: Connects Panther Labs' SIEM for alert investigation.

Code-Writing & Revision Domain

• Serena: Developer-assistant MCP for project search, editing, and symbol lookup.
• Coding-agent-mcp: File I/O, terminal, and repository operations with sandboxed environments.
• Next-devtools-mcp: Tailored for Next.js apps with project structure exploration.
• VS Code MCP Server: Direct integration with VS Code for reading, editing, and linting code.
• Code-to-tree: Parses source code into language-agnostic ASTs for semantic reasoning.

General MCPs

• 1mcp/agent: Aggregates multiple MCP servers under one unified endpoint.
• Mcp-server-browserbase: Browser automation for browsing, scraping, and form filling.
• Mcp-server-cloudflare: Integrates Cloudflare Workers, KV, R2, and D1 APIs.
• Supabase-mcp-server: Query, manage, and configure Supabase

Best Practices to Use MCP’s in Production

• While MCPs are helpful, you should avoid connecting them to your production without taking proper precautions. Here are several ways to use MCP servers securely:
• Use clear access controls: Define which users or systems can access each MCP server and what operations they can perform. Use scoped API keys or tokens, rotate them regularly, and store them securely in a secret manager.
• Keep servers isolated: Deploy each MCP server in its own environment or container to prevent one from affecting another. Use network segmentation or firewalls to limit communication to only what’s necessary.
• Monitor logs and performance: Collect logs for every request and response to help with troubleshooting and audits. Track performance metrics like latency, error rates, and uptime, and set alerts for unusual behavior.
• Validate inputs and outputs: Sanitize all incoming data and carefully review what your MCP servers return. Avoid exposing sensitive information and set sensible limits on data size to prevent overload or data leaks.
• Test before deployment: Always test in a staging or pre-production environment using realistic workloads. Include security checks, load testing, and compatibility verification with your AI assistant.
• Maintain consistent versions: Keep all MCP servers and clients on compatible versions. Apply updates promptly to fix bugs, security issues, or protocol mismatches, and document any configuration changes.
• Plan for failure: Set up retries and timeouts for network calls, and ensure services shut down gracefully. Back up configurations and important data regularly so you can recover quickly from incidents.

Final Words

Software developers, engineers and users are using MCP servers to talk to the product directly from an AI app. If you are planning to develop the MCP servers for your product and encounter challenges, our AI engineering team can provide expert support and end-to-end development assistance.

As our engineering team continues to discover and experiment with new MCP servers, we will keep updating the list. Contributions and recommendations from the community are always welcome. If you believe that we missed any MCP servers that deserve to be featured in this list, share it with me on LinkedIn.