Dynatrace Model Context Protocol (MCP) Server for AI assistants providing observability and monitoring capabilities with OpenTelemetry instrumentation
The server implements 26 tools with basic MCP compliance but significant definition quality gaps. Naming follows verb_noun conventions appropriately (get_*, list_*, create_*, etc.), which is positive. However, schema definitions are minimal, input parameters lack detailed type specifications, constraints, and validation guidance. Descriptions exist but are generic and often under 100 characters, offering limited LLM guidance on when/why to use each tool. Critical issues include: (1) No visible output schema documentation for any tool, LLMs cannot predict return structure; (2) Parameters often lack descriptions or type constraints (e.g., 'updates' in update_workflow is defined as 'type:object' with no field guidance); (3) No error handling guidance, tools return raw API errors without recovery hints; (4) Destructive tools (bulk_delete_dashboards, reset_grail_budget_tracker, execute_typescript) lack confirmation patterns or dry-run safeguards; (5) Several parameters accept free-form strings where enums would improve safety (e.g., 'problemType', 'access', 'channel'). The server demonstrates functional tool organization but lacks the polish expected of production-grade tooling.
Add tags to a monitored entity in Dynatrace
Delete multiple dashboards/documents from Dynatrace
Chat with Davis CoPilot for AI-powered assistance with Dynatrace
Create a dashboard in Dynatrace from a JSON configuration file
Create a workflow in Dynatrace to notify a team on specific problem types
Share a Dynatrace document directly with specified recipients
No output schema documentation. LLMs cannot predict return types, field names, or nested structure for any of the 26 tools. Forces agents to guess and risks failed data extraction.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 53 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 48 | - | v1 |
Execute a Dynatrace Query Language (DQL) statement and return results
Execute TypeScript code using Dynatrace App Engine Function Executor
Explain a DQL query in natural language using Davis CoPilot AI
Find monitored entities by name across all entity types
Generate DQL (Dynatrace Query Language) from natural language description using Davis CoPilot AI
Get the current GRAIL budget status and remaining allocation
Get warning status for GRAIL budget usage
Get information about the connected Dynatrace Environment (Tenant)
Get events for a Kubernetes cluster
Get the current GRAIL (Dynatrace data platform) budget tracking information
Get detailed information about a specific monitored entity
Get ownership information for specified entities
List problems from the Dynatrace environment with optional filtering by entity
List security vulnerabilities from the Dynatrace environment
Reset the GRAIL budget tracking counters
Send an email via Dynatrace email notification
Send a message to a Slack channel via Dynatrace webhook connection
Share a Dynatrace document (dashboard) with specific environments
Update an existing Dynatrace workflow
Verify if a DQL statement is syntactically valid
Destructive and sensitive tools lack confirmation/dry-run patterns. bulk_delete_dashboards, reset_grail_budget_tracker, and execute_typescript can cause data loss or arbitrary code execution without safeguards.
Parameter 'updates' in update_workflow is defined as bare 'object' with no field documentation. LLMs cannot construct valid payloads without knowing allowed fields, types, and constraints.
Free-form string parameters where enums would prevent hallucination: 'problemType' (should enum: AVAILABILITY|ERROR|SLOWDOWN|SECURITY), 'access' (should enum: read|read-write), 'channel' in send_slack_message (requires validation against available channels).
No error handling guidance. Tools return raw API errors without actionable recovery hints. LLMs cannot determine if errors are retryable, require user action, or indicate missing permissions.
Parameter descriptions are generic or missing context. E.g., 'The ID of the monitored entity' does not explain format, lookup method, or examples. Descriptions under 100 chars offer minimal LLM guidance.
No pagination documentation. Tools like list_problems and list_vulnerabilities return lists but lack limit, offset, and total_count parameters/fields. Large result sets risk context window exhaustion.
execute_typescript tool accepts arbitrary TypeScript code as a parameter. No syntax validation, no whitelist of allowed APIs, no resource limits. High injection and abuse risk.