--- title: From symptom to fix in minutes: AI-assisted development... description: A walkthrough shows how the Dynatrace MCP Server connects AI coding assistants to live observability data, letting developers ask plain-language questions... canonical: https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: From symptom to fix in minutes: AI-assisted development with Dynatrace MCP | daily.dev og:description: A walkthrough shows how the Dynatrace MCP Server connects AI coding assistants to live observability data, letting developers ask plain-language questions... og:url: https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35 og:image: https://api.daily.dev/og/posts/ij17sRO35.png og:image:alt: From symptom to fix in minutes: AI-assisted development with Dynatrace MCP og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # From symptom to fix in minutes: AI-assisted development with Dynatrace MCP **[Dynatrace](https://daily.dev/sources/dynatrace)** · 7 min read · 0 upvotes · 0 comments ## Summary A walkthrough shows how the Dynatrace MCP Server connects AI coding assistants to live observability data, letting developers ask plain-language questions about production performance problems. Using a sample multi-service case-management app (Node.js gateway, PostgreSQL-backed case-service, document-service), the MCP server surfaced top CPU-consuming database queries and mapped them to specific source lines. Two fixes are demonstrated: parallelizing four sequential aggregation queries (cutting response time ~75%) and adding PostgreSQL trigram indexes via pg_trgm to speed up ILIKE searches, eliminating full table scans that were driving ~67% database CPU usage. Dynatrace also publishes a set of 'skills' that teach coding agents how to query telemetry, correlate symptoms to code, and verify fixes. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I speed up a PostgreSQL ILIKE search that's causing full table scans and high CPU usage? Add a trigram index using the pg_trgm extension, since PostgreSQL cannot use a standard B-tree index for ILIKE pattern matching and otherwise reads every row on each search. Run CREATE EXTENSION IF NOT EXISTS pg_trgm, then CREATE INDEX CONCURRENTLY using gin with gin_trgm_ops on the searched columns. This requires no application code changes and can be applied to a live database without locking the table. _daily.dev surfaces practical postgres indexing fixes like this for developers chasing down slow queries._ ### How do I fix slow API response times caused by four sequential database aggregation queries running back-to-back? Run the independent aggregation queries in parallel instead of sequentially, since response time equals the sum of all four when fired one after another. Wrapping the four dbQuery calls in Promise.all so they execute concurrently cut the endpoint's response time by approximately 75% in one case-management application example. _developers optimizing slow endpoints can find similar query parallelization patterns on daily.dev._ ### What is the Dynatrace MCP Server and what can it do for debugging production issues? It is a tool that connects AI coding assistants directly to live Dynatrace observability data, letting developers ask plain-language questions about running systems and get answers backed by telemetry. It exposes entity relationships, service methods, SQL statements, and process CPU metrics, while a companion set of published skills teaches the AI agent how to correlate symptoms with specific lines of code and verify fixes. _engineers connecting AI agents to observability tooling can track MCP developments like this on daily.dev._ ## Similar posts on daily.dev - [Dynatrace MCP Server: Empower your AI assistants to interact with Dynatrace and access live production insights](https://daily.dev/posts/dynatrace-mcp-server-empower-your-ai-assistants-to-interact-with-dynatrace-and-access-live-producti-y2io1qtyy) · Dynatrace · 0 upvotes · 0 comments - [Four ways engineering teams use the Datadog MCP Server to power AI agents](https://daily.dev/posts/four-ways-engineering-teams-use-the-datadog-mcp-server-to-power-ai-agents-kxxssk2xn) · Datadog · 1 upvotes · 0 comments - [Bring real-time production insights into Claude Code with the Dynatrace MCP Server](https://daily.dev/posts/bring-real-time-production-insights-into-claude-code-with-the-dynatrace-mcp-server-sasg5foap) · Dynatrace · 2 upvotes · 0 comments - [Leverage AI for root-cause analysis with MCP servers in VS Code and Cursor](https://daily.dev/posts/leverage-ai-for-root-cause-analysis-with-mcp-servers-in-vs-code-and-cursor-yxyxaihaq) · Red Hat Developer · 0 upvotes · 0 comments - [Root Cause Analysis \(RCA\) in Plain English: Postman Agent Mode Meets Dynatrace MCP](https://daily.dev/posts/root-cause-analysis-rca-in-plain-english-postman-agent-mode-meets-dynatrace-mcp-sf0xp7et1) · ITNEXT · 0 upvotes · 0 comments --- Tags: [#devops](https://daily.dev/tags/devops), [#ai-agents](https://daily.dev/tags/ai-agents), [#postgresql](https://daily.dev/tags/postgresql), [#observability](https://daily.dev/tags/observability), [#mcp](https://daily.dev/tags/mcp) [View this post on daily.dev](https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"From symptom to fix in minutes: AI-assisted development with Dynatrace MCP","url":"https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35"},"datePublished":"2026-10-02T16:00:52.972Z","dateModified":"2026-10-02T16:03:04.901Z","description":"A walkthrough shows how the Dynatrace MCP Server connects AI coding assistants to live observability data, letting developers ask plain-language questions...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4e2e72ae7e01b0a321e6894a36992cf7?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4e2e72ae7e01b0a321e6894a36992cf7?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Dynatrace","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Dynatrace","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/3ca5a9897e9748f4a39fa9736389609e","url":"https://daily.dev/sources/dynatrace"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"devops,ai-agents,postgresql,observability,mcp","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Dynatrace","item":"https://daily.dev/sources/dynatrace"},{"@type":"ListItem","position":3,"name":"From symptom to fix in minutes: AI-assisted development with Dynatrace MCP"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/from-symptom-to-fix-in-minutes-ai-assisted-development-with-dynatrace-mcp-ij17sro35#faq","mainEntity":[{"@type":"Question","name":"How can I speed up a PostgreSQL ILIKE search that's causing full table scans and high CPU usage?","acceptedAnswer":{"@type":"Answer","text":"Add a trigram index using the pg_trgm extension, since PostgreSQL cannot use a standard B-tree index for ILIKE pattern matching and otherwise reads every row on each search. 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