Datadog MCP Server

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Join the Preview!

The Datadog MCP Server is in Preview. There is no charge for using the Datadog MCP Server during the Preview, but pricing may change when the feature becomes generally available. If you're interested in the MCP server and need access, complete this form.

Request Access

The Datadog MCP Server acts as a bridge between your observability data in Datadog and any AI agents that support the Model Context Protocol (MCP). Providing structured access to relevant Datadog contexts, features, and tools, the MCP Server lets you query and retrieve observability insights directly from AI-powered clients such as Cursor, OpenAI Codex, Claude Code, or your own AI agent.

Ready to get started? See Set Up the Datadog MCP Server for connection instructions.

This demo shows the Datadog MCP Server being used in Cursor and Claude Code (unmute for audio):

Disclaimers

The Datadog MCP Server is not supported for production use during the Preview.
Only Datadog organizations that have been specifically allowlisted can use the Datadog MCP Server. It is not available to the general public.
The Datadog MCP Server is HIPAA-eligible. You are responsible for ensuring that the AI tools you connect to the Datadog MCP Server meet your compliance requirements, such as HIPAA.
Datadog collects certain information about your usage of the Remote Datadog MCP Server, including how you interact with it, whether errors occurred while using it, what caused those errors, and user identifiers in accordance with the Datadog Privacy Policy and Datadog’s EULA. This data is used to help improve the server’s performance and features, including transitions to and from the server and the applicable Datadog login page for accessing the Services, and context (for example, user prompts) leading to the use of MCP tools. The data is stored for 120 days.

Requirements

Datadog users must have the MCP Read and/or MCP Write permissions to use the MCP server.

For setup instructions, see Set Up the Datadog MCP Server.

Toolsets

The Datadog MCP Server supports toolsets, which allow you to use only the tools you need, saving valuable context window space. These toolsets are available:

core: The default toolset for logs, metrics, traces, dashboards, monitors, incidents, hosts, services, events, and notebooks
alerting: Tools for validating monitors, searching monitor groups, and retrieving monitor templates
apm: Tools for in-depth APM trace analysis, span search, Watchdog insights, and performance investigation
dbm: Tools for interacting with Database Monitoring
error-tracking: Tools for interacting with Datadog Error Tracking
feature-flags: Tools for managing feature flags, including creating, listing, and updating flags and their environments
llmobs: Tools for searching and analyzing LLM Observability spans
product-analytics: Tools for interacting with Product Analytics queries
networks: Tools for Cloud Network Monitoring analysis and Network Device Monitoring
onboarding: Agentic onboarding tools for guided Datadog setup and configuration
security: Tools for code security scanning and searching security signals and security findings
software-delivery: Tools for interacting with Software Delivery (CI Visibility and Test Optimization)
synthetics: Tools for interacting with Datadog Synthetic tests

To use a toolset, include the toolsets query parameter in the endpoint URL when connecting to the MCP Server (remote authentication only). For example:

https://mcp.datadoghq.com/api/unstable/mcp-server/mcp retrieves only the core tools (this is the default if toolsets is not specified).
https://mcp.datadoghq.com/api/unstable/mcp-server/mcp?toolsets=synthetics retrieves only Synthetic Testing-related tools.
https://mcp.datadoghq.com/api/unstable/mcp-server/mcp?toolsets=core,synthetics,software-delivery retrieves core, Synthetic Testing, and Software Delivery tools.

Available tools

This section lists the tools available in the Datadog MCP Server and provides example prompts for using them.

Datadog MCP Server tools are under significant development and are subject to change. Use this feedback form to share any feedback, use cases, or issues encountered with your prompts and queries.

`search_datadog_events`

Toolset: core
Searches events like monitor alerts, deployment notifications, infrastructure changes, security findings, and service status changes.

Show me all deployment events from the last 24 hours.
Find events related to our production environment with error status.
Get events tagged with service:api from the past hour.

Note: See the Event Management API for more details.

`get_datadog_incident`

Toolset: core
Retrieves detailed information about an incident.

Get details for incident ABC123.
What’s the status of incident ABC123?
Retrieve full information about the Redis incident from yesterday.

Note: The tool is operational, but does not include incident timeline data.

`get_datadog_metric`

Toolset: core
Queries and analyzes historical or real-time metric data, supporting custom queries and aggregations.

Show me CPU utilization metrics for all hosts in the last 4 hours.
Get Redis latency metrics for the production environment.
Display memory usage trends for our database servers.

`get_datadog_metric_context`

Toolset: core
Retrieves detailed information about a metric including metadata, available tags, and tag values for filtering and grouping.

What tags are available for the system.cpu.user metric?
Show me all possible values for the env tag on redis.info.latency_ms.
Get metadata and dimensions for the requests.count metric.

`search_datadog_monitors`

Toolset: core
Retrieves information about Datadog monitors, including their statuses, thresholds, and alert conditions.

List all monitors that are currently alerting.
Show me monitors related to our payment service.
Find monitors tagged with team:infrastructure.

`get_datadog_trace`

Toolset: core
Fetches a complete trace from Datadog APM using a trace ID.

Get the complete trace for ID 7d5d747be160e280504c099d984bcfe0.
Show me all spans for trace abc123 with timing information.
Retrieve trace details including database queries for ID xyz789.

Note: Large traces with thousands of spans may be truncated (and indicated as such) without a way to retrieve all spans.

`search_datadog_dashboards`

Toolset: core
Lists available Datadog dashboards and key details.

Show me all available dashboards in our account.
List dashboards related to infrastructure monitoring.
Find shared dashboards for the engineering team.

Note: This tool lists relevant dashboards but provides limited detail about their contents.

`get_datadog_notebook`

Toolset: core
Retrieves detailed information about a specific notebook by ID, including name, status, and author.

Get details for notebook abc-123-def.
Show me the contents of the debugging notebook from yesterday.

`search_datadog_notebooks`

Toolset: core
Lists and searches Datadog notebooks with filtering by author, tags, and content.

Show me all notebooks created by the platform team.
Find notebooks related to performance investigation.
List notebooks tagged with incident-response.

`search_datadog_hosts`

Toolset: core
Lists and provides information about monitored hosts, supporting filtering and searching.

Show me all hosts in our production environment.
List unhealthy hosts that haven’t reported in the last hour.
Get all hosts tagged with role:database.

`search_datadog_incidents`

Toolset: core
Retrieves a list of Datadog incidents, including their state, severity, and metadata.

Show me all active incidents by severity.
List resolved incidents from the past week.
Find incidents that are customer-impacting.

`search_datadog_metrics`

Toolset: core
Lists available metrics, with options for filtering and metadata.

Show me all available Redis metrics.
List CPU-related metrics for our infrastructure.
Find metrics tagged with service:api.

`search_datadog_services`

Toolset: core
Lists services in Datadog’s Software Catalog with details and team information.

Show me all services in our microservices architecture.
List services owned by the platform team.
Find services related to payment processing.

`search_datadog_service_dependencies`

Toolset: core
Retrieves service dependencies (upstream/downstream) and services owned by a team.

Show me all upstream services that call the checkout service.
What downstream services does the payment API depend on?
List all services owned by the platform team.

`search_datadog_spans`

Toolset: core
Retrieves spans from APM traces with filters such as service, time, resource, and so on.

Show me spans with errors from the checkout service.
Find slow database queries in the last 30 minutes.
Get spans for failed API requests to our payment service.

`analyze_datadog_logs`

Toolset: core
Analyze Datadog logs using SQL queries for counting, aggregations, and numerical analysis. Use this for statistical analysis.

Count error logs by service in the last hour.
Show me the top 10 HTTP status codes with their counts.
Which services were logging the most during that time period?

`search_datadog_logs`

Toolset: core
Searches logs with filters (time, query, service, host, storage tier, and so on) and returns log details. Renamed from get_logs.

Show me error logs from the nginx service in the last hour.
Find logs containing ‘connection timeout’ from our API service.
Get all 500 status code logs from production.

`search_datadog_rum_events`

Toolset: core
Search Datadog RUM events using advanced query syntax.

Show JavaScript errors and console warnings in RUM.
Find pages that are loading slowly (more than 3 seconds).
Show recent user interactions on product detail pages.

`validate_datadog_monitor`

Toolset: alerting
Validates a monitor definition for correctness before creating or updating it.

Validate this monitor definition before I create it.
Check if my monitor query syntax is correct.

`get_datadog_monitor_templates`

Toolset: alerting
Retrieves available monitor templates to help you create monitors.

Show me the available monitor templates.
What templates can I use to create a new monitor?

`search_datadog_monitor_groups`

Toolset: alerting
Searches monitor groups by name or criteria.

Show me all monitor groups in an alerting state.
Find monitor groups related to the checkout service.

`apm_search_spans`

Toolset: apm
Searches for spans using APM query syntax, with support for pagination and tag filtering.

Show me spans with errors from the checkout service in the last hour.
Find slow database queries taking more than 2 seconds.
Search for spans with service:payments and status:error.

`apm_explore_trace`

Toolset: apm
Executes queries on trace data for deep analysis and exploration of specific spans within a trace.

Explore the spans in trace abc123 and show me the database calls.
Analyze the error spans in this trace.

`apm_trace_summary`

Toolset: apm
Generates an AI-powered summary of a trace, providing high-level analysis of what the trace shows.

Summarize trace 7d5d747be160e280504c099d984bcfe0.
Give me an overview of what happened in this trace.

`apm_trace_comparison`

Toolset: apm
Compares two traces to identify performance differences and bottlenecks between a fast trace and a slow trace.

Compare these two traces to find out why one is slower.
What changed between this baseline trace and the slow trace?

`apm_analyze_trace_metrics`

Toolset: apm
Analyzes APM trace metrics over time for a specific operation, querying metric data and providing AI-generated analysis.

Analyze latency trends for the web.request operation on service:api over the last 6 hours.
Show me error rate metrics for my database service.

`apm_discover_span_tags`

Toolset: apm
Discovers available tag keys on spans within a time range.

What tags are available on spans for service:checkout?
Show me the tag keys I can filter by in APM.

`apm_get_primary_tag_keys`

Toolset: apm
Retrieves the primary tag keys configured for the organization.

What are my organization’s primary tag keys?

`apm_search_watchdog_stories`

Toolset: apm
Searches for Watchdog anomaly detection stories for a service within a time range, providing AI-powered insights into latency, error rate, and traffic anomalies.

Show me Watchdog anomalies for the checkout service in the last 24 hours.
Are there any latency anomalies detected for my API service?

`apm_get_watchdog_story`

Toolset: apm
Retrieves detailed information about a specific Watchdog story by its ID.

Get the details of Watchdog story abc123.

`apm_search_change_stories`

Toolset: apm
Searches for change stories (deployments, feature flags, and infrastructure changes) for a service within a time range.

Show me recent deployments and changes for the payments service.
What infrastructure changes happened around the time of this latency spike?

`apm_latency_bottleneck_analysis`

Toolset: apm
Analyzes latency bottlenecks across traces in an anomaly period by calculating self-time.

What are the latency bottlenecks for the checkout service during this anomaly?
Identify which spans are contributing the most to latency.

`apm_latency_tag_analysis`

Toolset: apm
Compares span tags between an anomaly period and a baseline period to identify what changed.

Compare tags between the anomaly window and baseline to find what changed.
What tag values are different during this latency spike?

`apm_search_recommendations`

Toolset: apm
Searches for APM recommendations from Datadog.

Show me APM recommendations for my services.
Are there any optimization suggestions for my application?

`apm_get_recommendation`

Toolset: apm
Retrieves full details of a specific APM recommendation by ID.

Get the details of recommendation abc123.

`apm_investigation_methodology`

Toolset: apm
Provides guidance for investigating APM service issues like latency, errors, and performance problems.

How should I investigate a latency increase in my API service?
Guide me through debugging an error spike in production.

`search_datadog_dbm_plans`

Toolset: dbm
Searches Database Monitoring query execution plans, which show how the database engine executes queries, including index usage, join strategies, and cost estimates. Use this to analyze query performance and identify optimization opportunities.

Show me execution plans for slow queries on host:db-prod-1 from the last hour.
Find query plans with @db.plan.type:explain_analyze for the production database.
Get execution plans for queries by @db.user:app_user with duration greater than 1 second.

`search_datadog_dbm_samples`

Toolset: dbm
Searches Database Monitoring query samples, which represent individual query executions with performance metrics. Use this to analyze database activity patterns, identify slow queries, and investigate database performance issues.

Show me query samples with @duration:>1000000000 (duration greater than 1 second) from db:mydb.
Find slow queries on host:db-prod-1 filtered by @db.user:app_user.
Get recent query samples for @db.query_signature:abc123def and analyze performance patterns.

`search_datadog_error_tracking_issues`

Toolset: error-tracking
Searches Error Tracking Issues across data sources (RUM, Logs, Traces).

Show me all Error Tracking Issues in the checkout service from the last 24 hours.
What are the most common errors in my application over the past week?
Find Error Tracking Issues in the production environment with service:api.

`get_datadog_error_tracking_issue`

Toolset: error-tracking
Retrieves detailed information about a specific Error Tracking Issue from Datadog.

Help me solve Error Tracking Issue 550e8400-e29b-41d4-a716-446655440000.
What is the impact of Error Tracking Issue a3c8f5d2-1b4e-4c9a-8f7d-2e6b9a1c3d5f?
Create a test case to reproduce Error Tracking Issue 7b2d4f6e-9c1a-4e3b-8d5f-1a7c9e2b4d6f.

`list_datadog_feature_flags`

Toolset: feature-flags
Lists feature flags with pagination support.

Show me all feature flags in my organization.
List feature flags for the checkout service.

`get_datadog_feature_flag`

Toolset: feature-flags
Retrieves details about a specific feature flag.

Get details for the dark-mode-enabled feature flag.
What are the current settings for flag new-checkout-flow?

`create_datadog_feature_flag`

Toolset: feature-flags
Creates a new feature flag.

Create a feature flag called enable-new-dashboard for gradual rollout.
Set up a new boolean feature flag for the beta feature.

`list_datadog_feature_flag_environments`

Toolset: feature-flags
Lists environments configured for feature flags.

Show me the available feature flag environments.
What environments can I target with feature flags?

`list_datadog_feature_flag_allocations`

Toolset: feature-flags
Lists allocations for a feature flag in a specific environment.

Show me the allocation rules for flag new-checkout-flow in production.

`update_datadog_feature_flag_environment`

Toolset: feature-flags
Updates a feature flag configuration in a specific environment.

Enable the dark-mode flag in the staging environment.
Roll out flag new-checkout-flow to 50% of users in production.

`check_datadog_flag_implementation`

Toolset: feature-flags
Checks if a feature flag is implemented in code.

Verify that the enable-new-dashboard flag is implemented in my codebase.

`sync_datadog_feature_flag_allocations`

Toolset: feature-flags
Syncs feature flag allocations for a specific environment.

Sync the allocations for flag new-checkout-flow in production.

`search_datadog_llmobs_spans`

Toolset: llmobs
Retrieves and analyzes LLM Observability spans from Datadog, showing the complete request flow, model interactions, token usage, costs, and associated metadata.

Show me LLM Observability spans for my chatbot service in the last hour.
Find spans where the LLM model returned an error.
Analyze token usage and costs for my AI application over the past day.

`analyze_cloud_network_monitoring`

Toolset: networks
Investigates network-level issues using Cloud Network Monitoring data, analyzing network flow data to detect anomalies like elevated retransmission rates.

Analyze network traffic between my web servers and the database cluster.
Are there any retransmission issues between service:api and service:payments?
Investigate network flow data for anomalies in the production environment.

`search_ndm_devices`

Toolset: networks
Searches network devices (routers, switches, firewalls) monitored by Datadog Network Device Monitoring.

Show me all network devices in the us-east-1 datacenter.
Find firewalls that are reporting errors.
List all monitored switches and their statuses.

`get_ndm_device`

Toolset: networks
Retrieves detailed information about a specific network device by its device ID.

Get details for network device device:abc123.
Show me the configuration and status of this router.

`search_ndm_interfaces`

Toolset: networks
Retrieves all network interfaces for a specific device.

Show me all interfaces on device device:abc123.
List the interface statuses for my core router.

`browser_onboarding`

Toolset: onboarding
Guides you through onboarding Browser RUM to Datadog.

Help me set up Browser RUM monitoring for my web application.

`devices_onboarding`

Toolset: onboarding
Guides you through onboarding devices to Datadog monitoring.

Help me set up device monitoring in Datadog.

`kubernetes_onboarding`

Toolset: onboarding
Guides you through onboarding Kubernetes clusters to Datadog.

Help me set up Datadog monitoring for my Kubernetes cluster.

`llm_observability_onboarding`

Toolset: onboarding
Guides you through onboarding LLM Observability in Datadog.

Help me set up LLM Observability for my AI application.

`test_optimization_onboarding`

Toolset: onboarding
Guides you through onboarding Test Optimization in Datadog.

Help me set up Test Optimization for my CI pipeline.

`serverless_onboarding`

Toolset: onboarding
Guides you through onboarding serverless applications to Datadog.

Help me monitor my AWS Lambda functions with Datadog.

`source_map_uploads`

Toolset: onboarding
Guides you through uploading source maps for RUM error mapping.

Help me upload source maps so my RUM errors show original source code.

`datadog_code_security_scan`

Toolset: security
Runs a comprehensive security scan that detects both vulnerabilities (SQL injection, XSS, path traversal, and others) and secrets (API keys, passwords, credentials, and others) in parallel.

Scan my code for security vulnerabilities and hardcoded secrets.
Run a full security scan on this pull request.
Check this file for any security issues.

`datadog_sast_scan`

Toolset: security
Scans code for security vulnerabilities using static analysis (SAST), detecting SQL injection, XSS, path traversal, command injection, insecure cryptography, and other security weaknesses.

Scan this file for security vulnerabilities.
Check my code for SQL injection and XSS vulnerabilities.

`datadog_secrets_scan`

Toolset: security
Scans code for hardcoded secrets and credentials, detecting AWS keys, API keys, passwords, tokens, private keys, and database credentials.

Scan my code for hardcoded secrets.
Check if there are any API keys or passwords committed in this file.

`search_datadog_security_signals`

Toolset: security
Searches and retrieves security signals from Datadog Security Monitoring, including Cloud SIEM signals, App & API Protection signals, and Workload Protection signals.

Show me security signals from the last 24 hours.
Find high-severity security signals related to my production environment.
List Cloud SIEM signals triggered by suspicious login attempts.

`security_findings_schema`

Toolset: security
Returns the schema (available fields and their types) for security findings. Call this first before using analyze_security_findings to discover queryable fields. Supports filtering by finding type and controlling response size.

What fields are available for security findings?
Show me the schema for library vulnerability findings.
Get the full schema including descriptions for misconfiguration findings.

`analyze_security_findings`

Toolset: security
Primary tool for analyzing security findings using SQL queries. Queries live data from the last 24 hours with flexible SQL aggregations, filtering, and grouping. Call security_findings_schema first to discover available fields, then use this tool to query.

Show me the top 10 rules with the most critical findings.
Count open findings grouped by severity and finding type.
Find library vulnerabilities with exploits available, grouped by resource.

`search_security_findings`

Toolset: security
Fallback tool for retrieving full security finding details. Prefer analyze_security_findings for most analysis tasks. Use this tool only when you need complete finding objects or when SQL queries are insufficient.

Get full details for critical findings in my AWS environment.
Retrieve complete finding objects for a specific rule.
List all open identity risk findings with full metadata.

`search_datadog_ci_pipeline_events`

Toolset: software-delivery
Searches CI events with filters and returns details on them.

Show me all the pipelines for my commit 58b1488.
Show me the latest pipeline failure in branch my-branch.
Propose a fix for the job integration-test that fails every time on my branch my-branch.

`aggregate_datadog_ci_pipeline_events`

Toolset: software-delivery
Aggregates CI pipeline events to produce statistics, metrics, and grouped analytics.

What’s the average job duration for the last 7 days?
How many failed pipelines have there been in the last 2 weeks?
Show me the 95th percentile of pipeline duration grouped by pipeline name.

`get_datadog_flaky_tests`

Toolset: software-delivery
Searches Datadog Test Optimization for flaky tests and returns triage details (failure rate, category, owners, history, CI impact), with pagination and sorting.

Find active flaky tests for the checkout service owned by @team-abc, sorted by failure rate.
Show flaky tests on branch main for repo github.com/org/repo, most recent first.
List flaky tests in the timeout category with high failure rate (50%+) so I can prioritize fixes.

`aggregate_datadog_test_events`

Toolset: software-delivery
Aggregates Datadog Test Optimization events to quantify reliability and performance trends with aggregation functions, optional metrics, group-by facets, and configurable test levels.

Count the number of failed tests over the last week, grouped by branch.
Show me the 95th-percentile duration for each test suite to identify the slowest ones.
Count all passing and failing tests, grouped by code owners.

`search_datadog_test_events`

Toolset: software-delivery
Searches Test Optimization test events with filters and returns details on them.

Show me failed tests on branch main from the last 24 hours.
Get test executions for commit abc123 to see what passed and failed.
Show me all flaky test runs for the checkout service.
Find tests owned by @team-name that are failing.

`get_synthetics_tests`

Toolset: synthetics
Searches Datadog Synthetic tests.

Help me understand why the Synthetic test on endpoint /v1/my/tested/endpoint is failing.
There is an outage; find all the failing Synthetic tests on the domain api.mycompany.com.
Are Synthetic tests on my website api.mycompany.com still working in the past hour?

`edit_synthetics_tests`

Toolset: synthetics
Edits Datadog Synthetic HTTP API tests.

Improve the assertions of the Synthetic test defined on my endpoint /v1/my/tested/endpoint.
Pause the test aaa-bbb-ccc and set the locations to only European locations.
Add my team tag to the test aaa-bbb-ccc.

`synthetics_test_wizard`

Toolset: synthetics
Preview and create Datadog Synthetics HTTP API Tests.

Create Synthetics tests on every endpoint defined in this code file.
Create a Synthetics test on /path/to/endpoint.
Create a Synthetics test that checks if my domain mycompany.com stays up.

Context efficiency

The Datadog MCP Server is optimized to provide responses in a way that AI agents get relevant context without being overloaded with unnecessary information. For example:

Responses are truncated based on the estimated length of responses each tool provides. The tools respond to AI agents with instructions on how to request more information if the response was truncated.
Most tools have a max_tokens parameter that enables AI agents to request less or more information.

Track tool calls in Audit Trail

You can view information about calls made by MCP Server tools in Datadog’s Audit Trail. Search or filter by the event name MCP Server.

Feedback

The Datadog MCP Server is under significant development. During the Preview, use this feedback form to share any feedback, use cases, or issues encountered with your prompts and queries.