Best MCP Servers for Software Developers and 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

1. 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, enabling core activities like listing pods, services, deployments, retrieving logs, namespace management, and Helm v3 chart management.

2. GitHub MCP Server: Facilitates automating and managing GitHub repositories, issues, pull requests (PRs), branches, and releases via AI agents. The GitHub MCP Server specifically includes capabilities for managing organizations, repositories, and collaborators, optimizing GitHub workflows.

3. AWS MCP Server: Enables AI assistants to manage AWS resources such as S3, DynamoDB, VPC configurations, EC2, and IAM using natural language and programmatic interfaces.

4. Azure DevOps MCP Server: Integrates with Azure DevOps for managing work items, pipelines, repositories, and pull requests, using Python or TypeScript-based MCPs.

5. Terraform MCP Server: Integrates with the Terraform ecosystem to provide automation and interaction capabilities for Infrastructure as Code (IaC) development.

6. Jenkins MCP Server: Enables LLMs (like Claude or Cursor) to interact with Jenkins for tasks such as listing jobs, fetching job setups and build history, triggering builds with parameters, and retrieving build logs and status.

7. Argo CD MCP Server: Allows AI assistants to interact with Argo CD deployments and applications using natural language commands. It covers the Argo CD API and provides tools to list, create, sync, update, or delete apps, check resource trees and health, and retrieve logs for troubleshooting.

8. Docker Hub MCP Server: Docker Hub MCP Server is a Model Context Protocol (MCP) implementation that connects Docker Hub APIs to LLMs, enabling intelligent image discovery, repository management, and automation through natural language commands. It helps AI assistants like Docker’s Ask Gordon or tools such as Claude and VS Code fetch, manage, and analyze container data using real-time Docker Hub context.

9. Cyclops MCP Server: Enables AI agents to manage Kubernetes resources through the Cyclops abstraction layer, providing simplified operations for cluster management and application deployment workflows.

10. Alibaba Cloud MCP Server: Official Alibaba Cloud server allowing AI assistants to operate and manage Alibaba Cloud resources including ECS instances, Cloud Monitor metrics, OOS operations, and other widely used cloud products.

Testing and Validation Servers

1. Postman MCP Server: Postman MCP Server is a curated catalog on Postman listing official Model Context Protocol (MCP) servers, which allow AI agents to interact with external services via defined endpoints. It includes integrations for many platforms (e.g. GitHub, Google, AWS, Slack, etc.), letting users browse or “fork” usage collections for those MCP servers.

2. Testkube MCP Server: The MCP Server from Testkube enables AI assistants to directly interact with testing workflows, executions and artifacts on the Testkube platform. It lets agents execute and monitor test workflows, retrieve logs and artifacts, traverse history, and manage test resources, so AI tools can automate debugging, test-creation and workflow orchestration.

3. Playwright MCP Server: Official Microsoft implementation enabling LLMs to interact with web pages through structured accessibility snapshots, providing browser automation capabilities for testing and web interaction scenarios.

Monitoring & Observability Servers

1. Prometheus MCP Server: Enables LLMs like Claude to conduct standardized operations on a Prometheus server, including running PromQL queries, discovering accessible metrics, analyzing metadata, and getting instant or range query results.

2. Grafana MCP Server: Allows LLMs to interact programmatically with the Grafana environment, handling tasks such as searching and fetching dashboards by UID, listing and inspecting data sources, and querying Prometheus and Loki (metrics/logs).

3. Datadog MCP Server: Allows AI assistants to interact programmatically with Datadog, enabling operations like retrieving monitors, logs, metrics, dashboards, events, and incidents, and accessing monitor configurations and incident details.

4. Comet Opik MCP: Official Comet ML server enabling natural language exploration of LLM observability data, traces, and monitoring metrics captured by Opik, providing comprehensive AI model performance insights.

5. Influx DB MCP Server: Official InfluxDB server for InfluxDB 3 Core, Enterprise, and Cloud Dedicated deployments, enabling time-series data management and analysis through natural language queries.

6. Hydrolix MCP: Official Hydrolix integration providing time-series datalake schema exploration and query capabilities, enabling LLM-based workflows for large-scale time-series data analysis.

Daily Task Automation & Productivity Servers

1. Slack MCP Server: Connects AI models to Slack workspace, offering essential Slack features like channel listing, fetching channel history and thread messages, posting messages and responses, managing emoji reactions, and retrieving user information.

2. Filesystem MCP Server: Implements the MCP standard to enable secure and restricted file system activities, allowing AI agents to read and write files, manage directories, search for files, and retrieve metadata (permissions, timestamps, size).

3. Notion MCP Server: Serves as a bridge between AI agents and the Notion workspace, supporting core operations such as reading, creating, commenting, and managing pages and databases.

Database & Other Specific Use Cases

1. ClickHouse MCP Server: A specific implementation for ClickHouse database integration that provides safe query execution, read-only enforcement, and simplified database operations.

2. MCP Server Replicate: A Python-based MCP for AI model inference and image generation using FastMCP. It provides resource-based image generation and management, real-time updates via subscriptions, and progress and status monitoring.

3. La-rebellion MCP Server: A TypeScript-based implementation offering a simplified facade pattern for MCP integration, focusing on developer experience through streamlined API interaction and simple tool registration.

4. Chroma MCP Server : Access local and cloud Chroma vector database instances for retrieval operations.

5. Couchbase MCP Server : Manage Couchbase Capella and self-managed clusters for document operations.

6. Redis MCP: Official Redis implementation offering a standardized interface to manage and search data in Redis databases, supporting key-value operations, data structures, and search capabilities.

7. Snowflake MCP Server : Official Snowflake Labs open-source server supporting Cortex Agents prompting, structured and unstructured data querying, object management, SQL execution, semantic view querying, with full RBAC, fine-grained CRUD controls, and comprehensive authentication method support.

Security Domain

1. MCP-Scan: Scans MCP servers for vulnerabilities like prompt-injection or over-permissive tools, helping software developers secure their AI environments. Also features automatic dependency audits and configurable risk levels Scans MCP servers for vulnerabilities like prompt-injection or over-permissive tools, helping developers secure their AI environments. Also features automatic dependency audits and configurable risk levels

2. MCP Gateway: Acts as a security proxy that monitors and filters MCP tool requests, offering reputation scoring and real-time risk alerts. Supports centralized logging, rate-limiting, and access policy enforcement.

3. MCP for Security: Exposes security tools like Nmap and SQLMap to AI assistants, enabling prompt-driven penetration tests and vulnerability scans. Provides modular adapters for popular testing frameworks.

4. Contrast MCP Server: Integrates Contrast Security’s platform into MCP, enabling automated vulnerability detection and AI-guided remediation. Lets AI analyze runtime security data directly from Contrast dashboards.

5. Panther MCP Server: Connects Panther Labs’ SIEM to MCP, allowing AI to investigate alerts and tune detections programmatically. Features log enrichment, alert correlation, and threat-hunting queries.

Code-Writing & Revision Domain

1. Serena: A developer-assistant MCP offering project search, editing, and symbol lookup for codebases. Integrates with multiple editors and supports multi-file context reasoning. A developer-assistant MCP offering project search, editing, and symbol lookup for codebases. Integrates with multiple editors and supports multi-file context reasoning.

2. Coding-agent-mcp: Provides file I/O, terminal, and repository operations to AI agents for autonomous code management. Includes sandboxed environments for safe code execution and testing.

3. Next-devtools-mcp: Tailored for Next.js apps, it allows AIs to explore project structure, run dev builds, and update routes. Supports automated version migrations and component discovery.

4. VS Code MCP Server: Integrates directly with VS Code to let AI assistants read, edit, and lint code inside open workspaces. Exposes diagnostics, formatting, and Git operations to MCP clients.

5. Code-to-tree: Parses source code into language-agnostic ASTs so AIs can reason about structure instead of plain text. Enables semantic diffs and tree-level code navigation for refactoring.

General MCPs

1. 1mcp/agent: Aggregates multiple MCP servers under one unified endpoint for simplified multi-tool orchestration. Offers registry discovery, auto-auth, and server health monitoring.

2. Mcp-server-browserbase: Enables browser automation (browsing, scraping, form filling) through MCP. Can simulate sessions, take screenshots, and extract structured data.

3. Mcp-server-cloudflare: Integrates Cloudflare Workers, KV, R2, and D1 APIs into MCP for AI-driven cloud management. Supports DNS, security rules, and edge-deployment automation.

4. Supabase-mcp-server: Lets AIs query, manage, and configure Supabase databases and authentication settings. Includes schema introspection and row-level policy editing.

5. Redis Cloud API MCP Server: Interfaces with Redis Cloud APIs to manage instances, monitor memory usage, and tune performance. Provides real-time metrics and AI-triggered autoscaling recommendations.

Adding to these, here are a few hand-picked MCP Servers built by software developers and tech enthusiasts across the community, designed to automate operations, streamline development, enhance observability, and simplify cloud or infrastructure management. 

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.