Experiment with changelog

docs.json

@@ -36,7 +36,8 @@
                 ]
               },
               "platform-overview/security",
-              "platform-overview/roadmap"
+              "platform-overview/roadmap",
+              "platform-overview/changelog"
             ]
           },
           {

platform-overview/changelog.mdx

@@ -0,0 +1,75 @@
+---
+title: "Changelog"
+description: "This page explains the new features and improvements in Axiom."
+rss: true
+---
+
+<Update label="31 July, 2025" tags={["New features", "Improvements", "Fixes", "API"]} rss={{ title: "MCP Server", description: "Bring even more context from Axiom into your LLM clients with four new MCP tools." }}>
+
+<Frame>
+<img src="/doc-assets/changelog/changelog-mcp-new-tools.png" />
+</Frame>
+
+## MCP Server
+
+Back in December 2024, the Axiom MCP server started as a simple way to ask natural language questions and get powerful answers from your event data. Now, it’s becoming a lot more than that.
+
+We’ve added four new tools to expand what’s possible when connecting Axiom to your favorite LLM clients:
+
+- **`getDatasetSchema`** helps you explore unfamiliar datasets by revealing their structure up front.
+- **`getSavedQueries`** taps into your team’s existing knowledge, giving LLMs access to your most-used patterns.
+- **`getMonitors`** lets models understand what’s critical to your operations by pulling in your current monitors.
+- **`getMonitorsHistory`** provides visibility into how those monitors have behaved over time, making trends and anomalies easier to spot.
+
+These additions turn the Axiom MCP server from a basic querying interface into a well-rounded observability companion. Whether you’re debugging issues, summarizing incidents, or guiding an AI assistant through a dataset, you now have the tools to bring in the right context, right when it’s needed.
+
+Start building with the Axiom MCP server today. Check out the [open-source implementation](https://github.com/axiomhq/mcp-server-axiom) on GitHub.
+
+<AccordionGroup>
+    <Accordion title="Improvements">
+        Example
+    </Accordion>
+    <Accordion title="Fixes">
+        Example
+    </Accordion>
+    <Accordion title="API">
+        Example
+    </Accordion>
+</AccordionGroup>
+
+</Update>
+
+<Update label="31 July, 2025" tags={["Breaking changes"]} rss={{ title: "Update default version of OpenTelemetry semantic conventions", description: "We have updated the default version of OpenTelemetry semantic conventions from 1.32 to 1.33." }}>
+
+## Update default version of OpenTelemetry semantic conventions
+
+We have updated the default version of OpenTelemetry semantic conventions from 1.32 to 1.33. This means that the shape of your data can change when you don’t specify the version of the OTel semantic conventions in the data you send to Axiom. For more information, see [Semantic conventions](/docs/reference/semantic-conventions#changes-to-default-version).
+
+</Update>
+
+<Update label="16 July, 2025" tags={["New features"]} rss={{ title: "Spotlight", description: "Understand your event data at a glance with Axiom’s new Spotlight functionality." }}>
+
+## Spotlight
+
+<Frame>
+<img src="/doc-assets/changelog/changelog-spotlight.png" />
+</Frame>
+
+Axiom introduces Spotlight, a powerful new feature that transforms how you investigate issues by automatically surfacing the most relevant differences between selected events and baseline data.
+
+Instead of manually crafting queries to understand anomalies, Spotlight lets you select a region on any line chart or heatmap and immediately see what makes those events unique. Spotlight analyzes every field in your data and intelligently presents the most significant differences through adaptive visualizations.
+
+### Key use cases
+- **Root cause analysis:** Spotlight the slowest traces in your service to instantly see if they’re concentrated in a specific endpoint, region, or user segment—no complex queries required.
+- **Error investigation:** When errors spike, highlight that time period to automatically discover the underlying cause, whether it’s a new deployment, specific user actions, or infrastructure changes.
+- **Growth insights:** Spotlight an uptick in new sign-ups to quickly understand what’s different about your new users and optimize your acquisition strategy.
+
+### How it works
+
+1. **Select** any region on a line chart or heatmap where you want to investigate anomalous behavior
+2. **Run Spotlight** to automatically compare the selected events against your baseline data
+3. **Discover insights** as Spotlight ranks and visualizes the most significant differences across all fields
+
+Learn more about Spotlight in [our documentation](/console/intelligence/spotlight).
+
+</Update>