What can @dynatrace-oss/dynatrace-mcp-server do?

dynatrace api resource exposure via mcp protocol, mcp tool schema generation from dynatrace api specifications, dynatrace authentication and credential management for mcp clients, mcp resource discovery and listing for dynatrace entities, time-series metric query execution with temporal context, event and alert data retrieval with filtering and correlation, service and infrastructure topology querying, log data retrieval and search with structured filtering, slo and availability status querying

@dynatrace-oss/dynatrace-mcp-server

MCP ServerFree

Model Context Protocol (MCP) server for Dynatrace

Open Source

/ 100

9 capabilities

Capabilities9 decomposed

dynatrace api resource exposure via mcp protocol

Medium confidence

Exposes Dynatrace monitoring and observability APIs as MCP tools and resources, enabling LLM agents and Claude instances to query application performance monitoring data, infrastructure metrics, and log data through a standardized Model Context Protocol interface. Implements MCP server specification with tool definitions that map to Dynatrace REST API endpoints, allowing structured access to time-series metrics, event data, and topology information without direct API key exposure to the client.

Solves for

Query application performance metrics and SLO status from Dynatrace without writing custom API clientsRetrieve infrastructure and service topology data to inform incident response decisionsAccess historical monitoring data and logs to correlate with application issuesIntegrate Dynatrace observability data into LLM-powered incident investigation workflows+1 more

Best for

DevOps and SRE teams using Dynatrace for observability who want to integrate monitoring data into AI-assisted incident response

Platform engineers building internal tools that combine LLM reasoning with live infrastructure metrics

Organizations deploying Claude or other MCP-compatible AI agents for autonomous monitoring and alerting

Requires

Node.js 16+ (MCP server runtime)

Dynatrace tenant with API access enabled

Valid Dynatrace API token with read permissions for metrics, logs, and events

Limitations

Requires valid Dynatrace tenant URL and API token with appropriate scopes — no built-in token rotation or credential refresh

MCP protocol overhead adds latency compared to direct REST API calls; suitable for interactive queries, not real-time streaming metrics

Limited to Dynatrace API capabilities and rate limits; no local caching or aggregation layer for high-frequency queries

What makes it unique

Implements MCP server pattern specifically for Dynatrace, providing standardized tool definitions that abstract Dynatrace REST API complexity and enable LLM agents to query observability data without custom integration code. Uses MCP's resource and tool registry to expose Dynatrace capabilities as first-class LLM functions.

vs alternatives

Enables direct integration of Dynatrace data into Claude and other MCP-compatible LLMs without custom API wrappers, whereas traditional approaches require building bespoke integrations or using generic HTTP tool calling with manual API documentation.

mcp tool schema generation from dynatrace api specifications

Medium confidence

Automatically generates MCP-compliant tool schemas from Dynatrace API endpoint definitions, mapping REST API parameters, response structures, and authentication requirements into structured tool definitions that LLM clients can discover and invoke. Implements schema generation logic that translates Dynatrace API documentation into JSON Schema and MCP tool metadata, enabling dynamic tool registration without manual schema authoring.

Solves for

Automatically expose new Dynatrace API endpoints as MCP tools without manual schema definitionEnsure tool schemas remain synchronized with Dynatrace API changes and new featuresEnable LLM clients to discover available Dynatrace operations through MCP tool listingReduce maintenance burden of keeping tool definitions in sync with upstream API changes

Best for

Teams maintaining MCP servers that need to stay current with Dynatrace API evolution

Platform engineers building extensible observability integration layers

Organizations with multiple Dynatrace API consumers who want a single source of truth for tool definitions

Requires

Dynatrace API documentation or OpenAPI/Swagger specification (if available)

Node.js 16+ for schema generation tooling

Access to Dynatrace API metadata or specification files

Limitations

Schema generation accuracy depends on Dynatrace API documentation completeness — undocumented parameters or response fields may be missed

No built-in versioning or deprecation handling — breaking API changes require manual intervention or schema regeneration

Complex nested response structures may not translate perfectly to JSON Schema, requiring post-generation manual refinement

What makes it unique

Implements automated schema generation specifically for Dynatrace API surface, reducing manual effort to expose new endpoints as MCP tools. Uses introspection or specification-driven approach to generate tool definitions that remain maintainable as Dynatrace APIs evolve.

vs alternatives

Eliminates manual tool schema authoring for each Dynatrace API endpoint, whereas generic MCP servers require hand-crafted tool definitions for every new capability, creating maintenance overhead.

dynatrace authentication and credential management for mcp clients

Medium confidence

Manages Dynatrace API authentication (token-based) and credential handling within the MCP server, enabling secure credential injection into API requests without exposing tokens to LLM clients. Implements credential storage and request signing logic that intercepts MCP tool calls, injects Dynatrace API tokens, and forwards authenticated requests to Dynatrace endpoints, maintaining separation between client-facing MCP interface and backend authentication.

Solves for

Securely authenticate to Dynatrace API without exposing credentials to LLM clientsCentralize credential management so multiple LLM clients can share a single Dynatrace API tokenPrevent accidental credential leakage in LLM prompts or tool call logsSupport multiple Dynatrace tenants or API keys through configuration

Best for

Teams deploying MCP servers in shared or multi-tenant environments where credential isolation is critical

Organizations using Claude or other LLMs that should not have direct access to Dynatrace API keys

DevOps teams managing centralized observability access across multiple applications or teams

Requires

Dynatrace API token with appropriate read/write scopes

Secure credential storage mechanism (environment variables, secrets manager, or config file with restricted permissions)

Node.js process with access to credential storage at startup

Limitations

Credentials must be provisioned to the MCP server process at startup — no dynamic credential rotation without server restart

Token expiration or revocation requires manual server restart; no built-in refresh token handling

Credentials stored in environment variables or config files are vulnerable to process inspection or file system access

What makes it unique

Implements credential isolation pattern where MCP server acts as authentication proxy, accepting unauthenticated tool calls from LLM clients and injecting Dynatrace credentials server-side. Prevents credentials from being exposed to or logged by LLM clients.

vs alternatives

Provides credential isolation that generic HTTP tool calling or direct API integration cannot achieve, as those approaches require passing credentials to the LLM client or embedding them in prompts.

mcp resource discovery and listing for dynatrace entities

Medium confidence

Implements MCP resource protocol to expose Dynatrace entities (applications, services, hosts, dashboards, etc.) as discoverable resources that LLM clients can enumerate and reference. Uses MCP resource listing and URI scheme to represent Dynatrace entities as first-class resources, enabling LLM clients to browse available monitoring targets and construct context-aware queries without hardcoding entity names or IDs.

Solves for

Browse available applications, services, and infrastructure in Dynatrace through MCP resource listingDiscover entity IDs and metadata needed to construct targeted monitoring queriesEnable LLM clients to dynamically select which Dynatrace entities to query based on user intentProvide human-readable entity names and descriptions for LLM context

Best for

Interactive LLM applications where users need to select which Dynatrace entities to monitor

Agents that need to discover available monitoring targets before constructing queries

Multi-tenant environments where entity discovery helps LLM clients understand available scope

Requires

Dynatrace API token with read permissions for entity listing

MCP client that supports resource protocol (Claude, LLM frameworks with MCP resource support)

Limitations

Resource listing may be slow for Dynatrace environments with thousands of entities — no built-in pagination or filtering at MCP level

Resource URIs are static snapshots; entity changes in Dynatrace are not reflected until resource list is refreshed

No built-in caching of resource lists — each discovery request may trigger full API enumeration

What makes it unique

Exposes Dynatrace entities as MCP resources with URI scheme, enabling LLM clients to discover and reference monitoring targets through standardized resource protocol rather than requiring manual entity ID lookup or hardcoding.

vs alternatives

Provides structured entity discovery that generic tool calling cannot match, as LLM clients can browse available entities and construct context-aware queries, whereas direct API integration requires users to provide entity IDs upfront.

time-series metric query execution with temporal context

Medium confidence

Executes Dynatrace time-series metric queries through MCP tools, accepting time range specifications and metric selectors, and returning aggregated metric data with timestamps. Implements query parameter mapping that translates LLM-friendly time specifications (e.g., 'last 1 hour', 'last 7 days') into Dynatrace API time range parameters, and handles metric aggregation and downsampling based on query scope.

Solves for

Query application performance metrics (response time, error rate, throughput) for specific time windowsRetrieve infrastructure metrics (CPU, memory, disk) to diagnose resource constraintsCompare metric values across different time periods to identify trends or anomaliesAggregate metrics across multiple entities (services, hosts) to understand system-wide behavior

Best for

Incident response workflows where LLM agents need to retrieve historical metrics to diagnose issues

Automated monitoring dashboards that query Dynatrace metrics on demand

SRE teams building AI-assisted troubleshooting tools that correlate metrics with events

Requires

Dynatrace API token with metrics read permission

Valid metric selector syntax (Dynatrace metric expression language)

Time range specification (absolute timestamps or relative durations)

Limitations

Query latency depends on Dynatrace API response time and metric data availability — no local caching or pre-aggregation

Time range specifications are limited to Dynatrace API granularity; sub-second precision is not supported

Metric downsampling and aggregation follow Dynatrace defaults; custom aggregation functions require manual API parameter tuning

What makes it unique

Implements time-series metric querying through MCP tools with natural language time specification support (e.g., 'last 1 hour'), abstracting Dynatrace metric expression language and time range parameter complexity from LLM clients.

vs alternatives

Provides LLM-friendly metric querying that hides Dynatrace metric syntax and time parameter complexity, whereas direct API integration requires LLM clients to understand and construct Dynatrace metric expressions and Unix timestamp conversions.

event and alert data retrieval with filtering and correlation

Medium confidence

Retrieves Dynatrace events and alerts through MCP tools, supporting filtering by severity, entity type, time range, and custom tags. Implements event query logic that maps LLM-friendly filter specifications into Dynatrace event API parameters, and returns correlated event data with context (affected entities, root cause information, remediation suggestions if available).

Solves for

Retrieve recent alerts and events to understand current system health and ongoing incidentsFilter events by severity or entity type to focus on critical issuesCorrelate events across multiple services or infrastructure components to identify root causesAccess event history to understand incident patterns and recurring issues

Best for

Incident response workflows where LLM agents need to understand recent system events and alerts

Automated alerting systems that use LLM reasoning to correlate events and suggest remediation

SRE teams building AI-assisted incident investigation tools

Requires

Dynatrace API token with events read permission

Valid event filter parameters (severity, entity type, time range, tags)

Limitations

Event retrieval is limited to Dynatrace event retention policy — older events may not be available

Event filtering is constrained by Dynatrace event API capabilities; complex multi-field filters may not be supported

Event correlation is limited to what Dynatrace provides; custom correlation logic requires post-processing by LLM client

What makes it unique

Implements event and alert retrieval through MCP tools with LLM-friendly filter specifications, abstracting Dynatrace event API parameter complexity and providing correlated event context for incident investigation.

vs alternatives

Provides structured event retrieval with built-in filtering and correlation that generic tool calling cannot match, enabling LLM agents to quickly understand system events without manual API parameter construction.

service and infrastructure topology querying

Medium confidence

Queries Dynatrace service and infrastructure topology through MCP tools, returning dependency graphs, service relationships, and infrastructure hierarchy. Implements topology query logic that retrieves entity relationships from Dynatrace and formats them as graph or tree structures suitable for LLM reasoning about system architecture and impact analysis.

Solves for

Understand service dependencies to assess blast radius of potential changes or failuresIdentify critical path services that impact multiple downstream servicesVisualize infrastructure hierarchy (applications → services → hosts) for context during troubleshootingDetermine which services or infrastructure components are affected by a specific failure

Best for

Incident response workflows where LLM agents need to understand service dependencies and impact

Change management systems that use LLM reasoning to assess risk of infrastructure changes

Architecture documentation tools that automatically generate dependency diagrams from live Dynatrace data

Requires

Dynatrace API token with topology/entity read permission

Dynatrace instrumentation of services and infrastructure to be queried

Limitations

Topology data is a snapshot at query time; dynamic topology changes are not reflected until next query

Large topology graphs may be difficult for LLM clients to reason about; no built-in graph simplification or filtering

Topology accuracy depends on Dynatrace instrumentation coverage; uninstrumented services may not appear in topology

What makes it unique

Exposes Dynatrace topology and dependency data through MCP tools, enabling LLM agents to reason about service relationships and infrastructure hierarchy for impact analysis and incident investigation.

vs alternatives

Provides structured topology querying that enables LLM agents to understand service dependencies and impact, whereas generic observability tools require manual topology exploration or static documentation.

log data retrieval and search with structured filtering

Medium confidence

Retrieves log data from Dynatrace Logs through MCP tools, supporting structured filtering by log level, source, time range, and custom attributes. Implements log query logic that maps LLM-friendly filter specifications into Dynatrace Logs API parameters, and returns log records with context (source service, host, custom fields) suitable for incident investigation.

Solves for

Search application and infrastructure logs to find error messages and stack traces related to incidentsFilter logs by severity, source service, or time range to focus on relevant log entriesCorrelate log entries with metrics and events to understand root cause of issuesAccess structured log fields (request ID, user ID, custom attributes) for detailed investigation

Best for

Incident response workflows where LLM agents need to search logs for error context

Automated log analysis systems that use LLM reasoning to identify root causes

SRE teams building AI-assisted troubleshooting tools that correlate logs with metrics

Requires

Dynatrace API token with Logs read permission

Dynatrace Logs enabled and configured for the environment

Valid log filter parameters (log level, source, time range, custom attributes)

Limitations

Log retrieval is limited to Dynatrace Logs retention policy — older logs may not be available

Log search performance depends on query complexity and log volume; complex filters may timeout

Structured log field availability depends on application instrumentation; unstructured logs may not be filterable

What makes it unique

Implements log retrieval through MCP tools with structured filtering and LLM-friendly query specifications, abstracting Dynatrace Logs API complexity and providing context-rich log records for incident investigation.

vs alternatives

Provides structured log search with built-in filtering that generic tool calling cannot match, enabling LLM agents to efficiently search logs without manual API parameter construction or understanding Dynatrace query syntax.

slo and availability status querying

Medium confidence

Queries Dynatrace Service Level Objectives (SLOs) and availability status through MCP tools, returning current SLO compliance, error budgets, and availability metrics. Implements SLO query logic that retrieves SLO definitions and current status from Dynatrace, and formats results with compliance percentages and error budget remaining for LLM reasoning about service health.

Solves for

Check current SLO compliance status to understand if services are meeting availability targetsRetrieve error budget information to assess impact of incidents on SLO complianceIdentify services at risk of SLO breach to prioritize incident responseTrack SLO trends over time to understand service reliability patterns

Best for

SRE teams managing SLOs who want LLM-assisted incident prioritization based on SLO impact

Incident response workflows that use SLO status to determine severity and escalation

Automated alerting systems that trigger based on SLO breach risk

Requires

Dynatrace API token with SLO read permission

Dynatrace SLOs configured for the environment

Limitations

SLO query results are limited to SLOs defined in Dynatrace; custom SLO definitions external to Dynatrace are not accessible

SLO compliance calculation depends on Dynatrace metric data availability and accuracy

Error budget calculations are based on Dynatrace SLO definitions; custom error budget logic is not supported

What makes it unique

Exposes Dynatrace SLO and availability data through MCP tools, enabling LLM agents to reason about service reliability and SLO impact during incident response and prioritization.

vs alternatives

Provides structured SLO querying that enables LLM agents to assess SLO impact and error budget, whereas generic observability tools require manual SLO status checking or separate SLO management systems.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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1 shared capability

Best For

✓DevOps and SRE teams using Dynatrace for observability who want to integrate monitoring data into AI-assisted incident response
✓Platform engineers building internal tools that combine LLM reasoning with live infrastructure metrics
✓Organizations deploying Claude or other MCP-compatible AI agents for autonomous monitoring and alerting
✓Teams maintaining MCP servers that need to stay current with Dynatrace API evolution
✓Platform engineers building extensible observability integration layers
✓Organizations with multiple Dynatrace API consumers who want a single source of truth for tool definitions
✓Teams deploying MCP servers in shared or multi-tenant environments where credential isolation is critical
✓Organizations using Claude or other LLMs that should not have direct access to Dynatrace API keys

Known Limitations

⚠Requires valid Dynatrace tenant URL and API token with appropriate scopes — no built-in token rotation or credential refresh
⚠MCP protocol overhead adds latency compared to direct REST API calls; suitable for interactive queries, not real-time streaming metrics
⚠Limited to Dynatrace API capabilities and rate limits; no local caching or aggregation layer for high-frequency queries
⚠No built-in filtering or query optimization — clients must construct efficient Dynatrace API queries to avoid timeout or quota exhaustion
⚠Schema generation accuracy depends on Dynatrace API documentation completeness — undocumented parameters or response fields may be missed
⚠No built-in versioning or deprecation handling — breaking API changes require manual intervention or schema regeneration

Requirements

Node.js 16+ (MCP server runtime)Dynatrace tenant with API access enabledValid Dynatrace API token with read permissions for metrics, logs, and eventsMCP-compatible client (Claude, LLM framework with MCP support)Network connectivity to Dynatrace API endpointsDynatrace API documentation or OpenAPI/Swagger specification (if available)Node.js 16+ for schema generation toolingAccess to Dynatrace API metadata or specification files

Input / Output

Accepts: MCP tool calls with structured parameters (metric names, time ranges, entity filters), Natural language queries from LLM clients requesting observability data, Dynatrace API endpoint definitions or OpenAPI specifications, Configuration specifying which API endpoints to expose as tools, Dynatrace API token (provided via environment variable or configuration), MCP tool calls from LLM clients (without embedded credentials), MCP resource list requests from LLM clients, Optional filters or search parameters for entity discovery, Metric selector (e.g., 'builtin:service.response.time'), Time range (relative: 'last 1 hour', or absolute: Unix timestamps), Optional aggregation function (avg, max, min, sum), Optional entity filters (service name, host name, tags), Event filter criteria (severity: critical/warning/info, entity type, time range), Optional custom tag filters, Pagination parameters (limit, offset), Entity type (service, host, application), Optional entity filter (name, tag, custom attribute), Depth parameter (how many levels of dependencies to retrieve), Log filter criteria (log level: error/warning/info, source service/host, time range), Optional custom attribute filters, Optional search text (keyword search in log content), Optional SLO filter (name, tag, service), Optional time range for historical SLO status

Produces: Structured JSON responses containing metric values, timestamps, and metadata, Event and log records with severity, source, and context information, Topology and dependency data in graph or list format, MCP tool schema JSON with parameter definitions, descriptions, and response structures, Tool registry metadata for MCP server initialization, Authenticated HTTP requests to Dynatrace API, Responses from Dynatrace API forwarded to MCP clients, Structured list of Dynatrace entities with URIs, names, and metadata, Entity details (type, status, tags, custom attributes), Time-series data points with timestamp, metric value, and aggregation metadata, Metric statistics (min, max, avg) for the query time range, Event records with timestamp, severity, affected entity, description, and root cause information, Alert status and remediation suggestions (if available), Topology graph with nodes (entities) and edges (dependencies), Service relationship data (calls, dependencies, impact), Infrastructure hierarchy (applications → services → hosts), Log records with timestamp, log level, source, message, and structured fields, Log context (service name, host name, custom attributes), SLO definitions (name, target, time window), Current SLO compliance percentage, Error budget remaining (percentage or time), SLO status (compliant, at-risk, breached)

UnfragileRank

Adoption46%(30% weight)

Quality19%(25% weight)

Ecosystem42%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

9 capabilities

Visit @dynatrace-oss/dynatrace-mcp-server→

Package Details

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1.8.2

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Model Context Protocol (MCP) server for Dynatrace

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Capabilities9 decomposed

dynatrace api resource exposure via mcp protocol

Medium confidence

Solves for

Best for

DevOps and SRE teams using Dynatrace for observability who want to integrate monitoring data into AI-assisted incident response

Platform engineers building internal tools that combine LLM reasoning with live infrastructure metrics

Organizations deploying Claude or other MCP-compatible AI agents for autonomous monitoring and alerting

Requires

Node.js 16+ (MCP server runtime)

Dynatrace tenant with API access enabled

Valid Dynatrace API token with read permissions for metrics, logs, and events

Limitations

Requires valid Dynatrace tenant URL and API token with appropriate scopes — no built-in token rotation or credential refresh

MCP protocol overhead adds latency compared to direct REST API calls; suitable for interactive queries, not real-time streaming metrics

Limited to Dynatrace API capabilities and rate limits; no local caching or aggregation layer for high-frequency queries

What makes it unique

vs alternatives

mcp tool schema generation from dynatrace api specifications

Medium confidence

Solves for

Best for

Teams maintaining MCP servers that need to stay current with Dynatrace API evolution

Platform engineers building extensible observability integration layers

Organizations with multiple Dynatrace API consumers who want a single source of truth for tool definitions

Requires

Dynatrace API documentation or OpenAPI/Swagger specification (if available)

Node.js 16+ for schema generation tooling

Access to Dynatrace API metadata or specification files

Limitations

Schema generation accuracy depends on Dynatrace API documentation completeness — undocumented parameters or response fields may be missed

No built-in versioning or deprecation handling — breaking API changes require manual intervention or schema regeneration

Complex nested response structures may not translate perfectly to JSON Schema, requiring post-generation manual refinement

What makes it unique

vs alternatives

Eliminates manual tool schema authoring for each Dynatrace API endpoint, whereas generic MCP servers require hand-crafted tool definitions for every new capability, creating maintenance overhead.

dynatrace authentication and credential management for mcp clients

Medium confidence

Solves for

Best for

Teams deploying MCP servers in shared or multi-tenant environments where credential isolation is critical

Organizations using Claude or other LLMs that should not have direct access to Dynatrace API keys

DevOps teams managing centralized observability access across multiple applications or teams

Requires

Dynatrace API token with appropriate read/write scopes

Secure credential storage mechanism (environment variables, secrets manager, or config file with restricted permissions)

Node.js process with access to credential storage at startup

Limitations

Credentials must be provisioned to the MCP server process at startup — no dynamic credential rotation without server restart

Token expiration or revocation requires manual server restart; no built-in refresh token handling

Credentials stored in environment variables or config files are vulnerable to process inspection or file system access

What makes it unique

vs alternatives

Provides credential isolation that generic HTTP tool calling or direct API integration cannot achieve, as those approaches require passing credentials to the LLM client or embedding them in prompts.

mcp resource discovery and listing for dynatrace entities

Medium confidence

Solves for

Best for

Interactive LLM applications where users need to select which Dynatrace entities to monitor

Agents that need to discover available monitoring targets before constructing queries

Multi-tenant environments where entity discovery helps LLM clients understand available scope

Requires

Dynatrace API token with read permissions for entity listing

MCP client that supports resource protocol (Claude, LLM frameworks with MCP resource support)

Limitations

Resource listing may be slow for Dynatrace environments with thousands of entities — no built-in pagination or filtering at MCP level

Resource URIs are static snapshots; entity changes in Dynatrace are not reflected until resource list is refreshed

No built-in caching of resource lists — each discovery request may trigger full API enumeration

What makes it unique

vs alternatives

time-series metric query execution with temporal context

Medium confidence

Solves for

Best for

Incident response workflows where LLM agents need to retrieve historical metrics to diagnose issues

Automated monitoring dashboards that query Dynatrace metrics on demand

SRE teams building AI-assisted troubleshooting tools that correlate metrics with events

Requires

Dynatrace API token with metrics read permission

Valid metric selector syntax (Dynatrace metric expression language)

Time range specification (absolute timestamps or relative durations)

Limitations

Query latency depends on Dynatrace API response time and metric data availability — no local caching or pre-aggregation

Time range specifications are limited to Dynatrace API granularity; sub-second precision is not supported

Metric downsampling and aggregation follow Dynatrace defaults; custom aggregation functions require manual API parameter tuning

What makes it unique

vs alternatives

event and alert data retrieval with filtering and correlation

Medium confidence

Solves for

Best for

Incident response workflows where LLM agents need to understand recent system events and alerts

Automated alerting systems that use LLM reasoning to correlate events and suggest remediation

SRE teams building AI-assisted incident investigation tools

Requires

Dynatrace API token with events read permission

Valid event filter parameters (severity, entity type, time range, tags)

Limitations

Event retrieval is limited to Dynatrace event retention policy — older events may not be available

Event filtering is constrained by Dynatrace event API capabilities; complex multi-field filters may not be supported

Event correlation is limited to what Dynatrace provides; custom correlation logic requires post-processing by LLM client

What makes it unique

vs alternatives

service and infrastructure topology querying

Medium confidence

Solves for

Best for

Incident response workflows where LLM agents need to understand service dependencies and impact

Change management systems that use LLM reasoning to assess risk of infrastructure changes

Architecture documentation tools that automatically generate dependency diagrams from live Dynatrace data

Requires

Dynatrace API token with topology/entity read permission

Dynatrace instrumentation of services and infrastructure to be queried

Limitations

Topology data is a snapshot at query time; dynamic topology changes are not reflected until next query

Large topology graphs may be difficult for LLM clients to reason about; no built-in graph simplification or filtering

Topology accuracy depends on Dynatrace instrumentation coverage; uninstrumented services may not appear in topology

What makes it unique

vs alternatives

log data retrieval and search with structured filtering

Medium confidence

Solves for

Best for

Incident response workflows where LLM agents need to search logs for error context

Automated log analysis systems that use LLM reasoning to identify root causes

SRE teams building AI-assisted troubleshooting tools that correlate logs with metrics

Requires

Dynatrace API token with Logs read permission

Dynatrace Logs enabled and configured for the environment

Valid log filter parameters (log level, source, time range, custom attributes)

Limitations

Log retrieval is limited to Dynatrace Logs retention policy — older logs may not be available

Log search performance depends on query complexity and log volume; complex filters may timeout

Structured log field availability depends on application instrumentation; unstructured logs may not be filterable

What makes it unique

vs alternatives

slo and availability status querying

Medium confidence

Solves for

Best for

SRE teams managing SLOs who want LLM-assisted incident prioritization based on SLO impact

Incident response workflows that use SLO status to determine severity and escalation

Automated alerting systems that trigger based on SLO breach risk

Requires

Dynatrace API token with SLO read permission

Dynatrace SLOs configured for the environment

Limitations

SLO query results are limited to SLOs defined in Dynatrace; custom SLO definitions external to Dynatrace are not accessible

SLO compliance calculation depends on Dynatrace metric data availability and accuracy

Error budget calculations are based on Dynatrace SLO definitions; custom error budget logic is not supported

What makes it unique

Exposes Dynatrace SLO and availability data through MCP tools, enabling LLM agents to reason about service reliability and SLO impact during incident response and prioritization.

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@dynatrace-oss/dynatrace-mcp-server

Capabilities9 decomposed

dynatrace api resource exposure via mcp protocol

mcp tool schema generation from dynatrace api specifications

dynatrace authentication and credential management for mcp clients

mcp resource discovery and listing for dynatrace entities

time-series metric query execution with temporal context

event and alert data retrieval with filtering and correlation

service and infrastructure topology querying

log data retrieval and search with structured filtering

slo and availability status querying

Related Artifactssharing capabilities

@dynatrace-oss/dynatrace-mcp-server

datadog-mcp-server

dbt

SchemaCrawler

1mcpserver

@winor30/mcp-server-datadog

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @dynatrace-oss/dynatrace-mcp-server

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@dynatrace-oss/dynatrace-mcp-server

Capabilities9 decomposed

dynatrace api resource exposure via mcp protocol

mcp tool schema generation from dynatrace api specifications

dynatrace authentication and credential management for mcp clients

mcp resource discovery and listing for dynatrace entities

time-series metric query execution with temporal context

event and alert data retrieval with filtering and correlation

service and infrastructure topology querying

log data retrieval and search with structured filtering

slo and availability status querying

Related Artifactssharing capabilities

@dynatrace-oss/dynatrace-mcp-server

datadog-mcp-server

dbt

SchemaCrawler

1mcpserver

@winor30/mcp-server-datadog

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