Boost KQL Queries: Enhanced Documentation

Hey guys, let's dive into a cool project aimed at making KQL queries a breeze, especially for language models. We're talking about enhancing the existing documentation to help these models write spot-on KQL queries. The goal? To make your code searches more accurate and less of a headache. This is a super practical project, a zero-cost solution that can significantly improve how our language models interact with KQL. So, if you're ready to explore how we can make our semantic code search even better, let's get started.

The KQL Query Conundrum: Why Language Models Struggle

So, why are language models sometimes tripping over KQL queries? The answer lies in the nitty-gritty details of KQL syntax. First off, KQL demands precision. Think of it like a secret code, where the smallest mistake can throw everything off. For example, if you forget the quotes around your wildcards, the query might fail. Or, if you use uppercase for boolean operators, the query won't work as intended. These are common issues that lower-tier language models often struggle with. They are the main challenges we are looking to solve.

Secondly, the current documentation lacks inline examples. Sure, it tells you what KQL can do, but it doesn't always show you how to do it with concrete examples. It's like learning to bake without a recipe. You know what you want to make, but you're missing the step-by-step guide. Finally, there's a lack of reference for common patterns. This means the models must either experiment to find the correct syntax or guess which can lead to failed queries.

As a result of these challenges, we see a lot of repeated errors like the use of incorrect operators, confusing field usage, and missing quotations which all contribute to the problem. With this enhanced documentation, we aim to address these issues head-on by providing clear, concise instructions and examples. This will give the language models the information they need to quickly and accurately build their queries.

Zero-Cost Solution: Enhancing KQL Documentation

This project is awesome because it's a zero-cost solution. We don't need new tools or API changes. It's all about improving the existing documentation to help language models succeed. This is a low-effort, high-impact solution that benefits everyone. The focus here is on making the existing tools more effective with the existing resources. We are looking for improvement, and it is important to provide them in the most efficient way possible. The core idea is to enhance the tool instructions with an inline KQL quick reference. This reference will be embedded directly into the tool instructions, ensuring it's always accessible to language models when they use semantic code search tools.

The Implementation Details: Where and How

Let's break down the implementation details. First, the location. We're adding the enhanced KQL documentation to the system prompt within the existing tool usage instructions. Or, we'll create a new <kql_reference> section that's always visible to the model. The goal is to make the reference easily accessible, so the language models can always refer to it when needed.

Next, the content. The complete KQL Quick Reference content is available. This content will be embedded directly into the tool instructions, ensuring it's always visible to language models when they use semantic code search tools. We will provide many examples to help the language models. The goal is to provide the language models with all of the information they need to succeed with KQL queries. We want to build a more user-friendly experience for everybody. We are providing solutions for a more efficient and accessible system.

The project involves modifying specific files, specifically src/mcp_server/server.ts. Here, we will add the KQL reference to the tool descriptions. We might also update the README to document these enhancements. The beauty of this project is that it involves no breaking changes. It's a purely additive documentation enhancement. We're not changing any existing functionality but simply making it easier for the language models to use what's already there.

Real-World Context: How This Project Came About

This engineering document is based on a real-world testing session. We successfully explored a 3,000-file analytics repository using the Semantic Code Search MCP server with Claude Sonnet 4.5. The markdown boost feature was working effectively, surfacing documentation before code. However, the main pain point identified was KQL syntax complexity for lower-tier models, leading to this proposal for enhanced inline documentation.

We found that, despite the models' capabilities, KQL syntax could be a barrier. The models understood the concepts, but they struggled with the specific syntax. This project addresses that challenge directly. By providing clear, concise examples and quick references, we can help these models overcome this hurdle and be more effective. It's a simple yet effective way to improve the performance of our semantic code search tools, making the experience smoother and more intuitive for everyone.

The Benefits: Why This Matters

So, why does this matter? Why are we taking the time to improve KQL documentation? The answer is simple: it leads to better results. By making KQL queries easier to understand and use, we improve the accuracy and efficiency of our semantic code searches. This means less time spent troubleshooting syntax errors and more time getting valuable insights from our data. It's all about optimizing the user experience and making sure that our tools are as effective as possible. In short, enhanced KQL documentation is an investment in efficiency, accuracy, and user satisfaction.

Conclusion: A Path to Better Code Search

So, there you have it, guys. Enhancing KQL documentation is a smart, practical move to improve our code search capabilities. With a few simple additions to our documentation, we can help language models write better queries, saving time and increasing accuracy. It's a testament to the fact that sometimes the simplest solutions are the most effective. We hope you are as excited about this project as we are. If you have any questions or want to learn more, please reach out. We're always here to help make our tools the best they can be.

For further reading on KQL and related topics, check out the Elastic documentation. This is a great resource to understand KQL better.

Elastic KQL Documentation