In terms of capabilities, I was curious and amazed — but I made sure not to become that junior who blindly treats it as just another hype wave. So instead of installing everything at once I was looking across different scenarios and strategies, then I mapped specific MCP integrations to each category correspondingly:

• Documentation

• Problem solving

• Code quality & Security

• Observability & Debugging

And so on... But the goal here is not to list every possible tool available for every scenario, like shooting in the dark, but to show a practical approach to mapping these.

note*

Even though the protocol is still evolving, some tools may not yet provide direct marketplace integrations. In those cases, most of them can be integrated either through IDE integrations, local services using Docker, or configuration files such as .yaml or .json.

What kind of tools I was actually using?

1. Understanding documentation — Context7 MCP

LLMs may sometimes return generic or outdated information when handling certain requests by default, and in this case I didn't want to waste tokens that easily, right? And without a proper context it can be intensified.

Context7 is a tool that automatically enables my coding assistant to get essential information straight from the official documentation about any library and stay up-to-dated sequentially.

How it helps me:

• Ask the tool to quickly get essential information from official documentation.

• Verify the latest updates or changes in libraries without wasting time.

• Cross-check code examples to ensure correct usage of functions or APIs.

• Explore sequential explanations to understand complex workflows.

• Reduce guesswork by having context-aware guidance for coding tasks.

2. Technical questions & resolutions — Stack Overflow MCP

Furthermore, the process of dealing with errors tends to be quite similar for every developer. Even after carefully reading the documentation and trying to understand how something should work, I may eventually reach a point where it is still unclear how to implement a feature correctly.

And that's why Stack Overflow is widely used to explore solutions and learn from the approaches shared by other developers who have faced similar problems.

You can simply ask your IDE to...

• Find solutions to errors or bugs you encounter during development.

• Compare multiple answers and highlight the most practical solution for your case.

• Show best practices and patterns used in real projects.

• Clarify unclear aspects of library usage or framework implementation.

3. Code Quality & Security — Codacy MCP

Maintaining good code quality is another important aspect of the development workflow. As projects grow, it becomes easier to introduce small issues such as inconsistent patterns, potential bugs, or security risks.

Tools like Codacy help automate parts of this process by analyzing the codebase and highlighting areas that may require attention. This includes identifying code smells, possible vulnerabilities, and maintainability concerns.

By integrating automated analysis into the workflow, it becomes easier to maintain consistent standards and improve the overall reliability of the project over time.

How I use it to keep code reliable?

• Ask the IDE to check your code for style issues, complexity, and performance problems.

• Review pull requests to see issues introduced or fixed before merging.

• Audit code quality to find technical debt and areas that need improvement.

• Check test coverage for files or entire repositories.

• Spot code duplicates to reduce redundancy.

• Scan for security vulnerabilities, such as secrets, dependency issues, and other risks.

4. Observability — OpenTelemetry MCP

Understanding how an application behaves in practice is also a key part of modern development, especially when dealing with distributed systems or microservices.

Frameworks such as OpenTelemetry make it possible to collect telemetry data such as traces, metrics, and logs, allowing developers to analyze request flows, latency patterns, and system performance.

This kind of visibility helps identify issues that may not be obvious during development, such as bottlenecks, unexpected latency spikes, or errors occurring across service boundaries.

How I gain visibility into my applications...

• Collect traces, metrics, and logs to understand application behavior.

• Analyze request flows and latency patterns across services.

• Detect bottlenecks, unexpected latency spikes, or cross-service errors.

• Correlate telemetry data to identify issues not visible during development.

• Monitor system performance in real time to improve reliability.

Final thoughts

Many ecosystems already provide their own MCP integrations, and developers can explore and adopt them depending on the stack they are working with.

To explore more possibilities around MCP servers, I recommend checking out this repository which served as reference:

https://github.com/punkpeye/awesome-mcp-servers