Systems | Development | Analytics | API | Testing

4 Tips for Developing Model Context Protocol Server

The Model Context Protocol (MCP) is rapidly becoming the connective tissue for agentic AI systems and IDE tooling. Whether you’re building a dev tool that integrates with LLMs or enabling a context-aware API backend, standing up an MCP server is a rite of passage. But MCP is still in its early days and there are some sharp edges. Here are four practical shortcuts to fast-track your MCP server development so you can skip the boilerplate and get to the good stuff: intelligent tooling.
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Modernize Test Data Management with Traffic Replay

In software testing or platform engineering, having realistic data is crucial. For years, teams have relied on Test Data Management (TDM) to copy entire production databases, scrub any sensitive information, and then spin up test environments from these sanitized data sets. While TDM gets the job done, it can be costly, time-consuming, and can quickly become outdated. The issue of outdated data becomes more pronounced as deployment velocity increases and back end dependencies become more diverse (think: microservices).

Using Proxymock with AWS Services

Amazon Web Services, or AWS, offers a variety of cloud services ranging from AWS resources such as CDNs and data lakes to cloud computing and transformation services such as compute resources, virtual servers, and dynamic availability zones. For this reason, AWS cloud is one of the most broadly adopted cloud solutions, offering a global network of solutions at generally lower costs compared to on-premises solutions.

Using Proxymock with GCP Services

Google Cloud Platform, or GCP, is a cloud resources collection offered by Google for enterprise and standard users. GCP offers a wide range of cloud services, including compute, storage, networking, security, analytics, and even machine learning models. Google Cloud products are the backbone of many cloud applications. Google Cloud allows flexibility with the scalable and predictable cost management.
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Six Lessons from Production gRPC

In the half-decade since gRPC became part of our production ecosystem, we've encountered a range of challenges and discovered a few hidden pitfalls that can trip up even the most experienced teams. Below, we'll walk through some of the core lessons learned, with tips, best practices, and examples drawn straight from the trenches.

Automating API Mocks in Your CI Pipeline with proxymock

When running tests in a CI/CD pipeline, relying on external APIs can introduce instability, slow down execution, and even lead to failed builds due to rate limits or API downtime. Fortunately proxymock provides a solution by capturing API interactions and running a local mock server, enabling fully isolated and repeatable tests. In this blog, we’ll demonstrate how to integrate proxymock into a GitHub Actions CI pipeline using a demo app called outerspace-go.

How to Mock AI APIs Using proxymock

APIs often represent the cutting edge of the technology space. This is especially true with Artificial Intelligence – as AI has evolved from speculative technology to mass adoption, it has shown up significantly in APIs as a modality and mechanism. However, as with all new technologies, using AI APIs comes with significant challenges.
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Ephemeral Environment Testing: Do you need it?

Traditional testing methods often delay the software development lifecycle, as we have grown used to these outdated processes without considering alternatives. Ephemeral environments introduce a more efficient solution. They allow for the quick creation and dismantling of isolated testing environments. These isolated environments approach leads to faster and more productive development cycles while still delivering high-quality software to users. In this article, we'll explore ephemeral environments, how they work, and why they might be the solution your team needs.

What Is Shadow Traffic? All You Need to Know

Production traffic can often be unpredictable, and distinguishing genuine user interactions from mere noise becomes a pivotal step in comprehensively grasping the types of requests and workflows occurring within your deployment. One important concept to explore in this context is shadow traffic, which plays a significant role in analytics and cybersecurity but is often misunderstood or rarely discussed.

Using Python MockServer for API Testing

Using a mock server is a popular method of working around these limitations and realities, allowing you to test web server assets against specific requests, ensuring that your response data matches the expected outcome. Today, we’re going to look at a powerful solution for Python clients in the form of MockServer. We’ll walk through the tool’s basics and learn how to use it for your own testing.