MCP100 IndexMonitoring & Observability

MCP100 SelectedFirst-party MCP

Datadog MCP

by Datadog

Updated July 30, 2026 · Reviewed July 24, 2026

Monitoring & ObservabilityHealthcare & Life SciencesSoftware & Technology

MCP100 verdict

Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.

MCP100 Assessment

Editorial verdict

Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.

Reviewed 2026-07-24 · Next review 2026-09-24

Task Fit

Mapped

Specialized workflows

Trust

Reviewed

A dated editorial review is published on the MCP detail page.

Maintenance

Date unavailable

No dated repository or package update is currently stored.

Popularity

Signals unavailable

No independently comparable adoption metric is currently stored.

Best for

Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.
Teams already using Datadog that can test with non-sensitive data and least privilege.

Not ideal for

Users who need an MCP100-selected recommendation today
Unattended production workflows before permissions and failure behavior are reviewed

Avoid if

The connected data or account cannot be safely exposed to the selected AI client
You cannot verify least-privilege access and review write-capable tool calls

Trade-offs

First-party provenance and a clear task improve confidence, but do not replace an independent permissions and usability review.
Client transport compatibility does not prove that authentication, scopes, failure handling, and every tool behave safely.

Evidence scope

Evidence

Current source snapshot

Popularity

No MCP-specific repository metric is stored

No MCP-specific package metric is stored

Stars observation date unavailable · Downloads observation date unavailable

The publisher provides a hosted implementation without a separate public repository metric.

The publisher does not document a separate comparable package metric for this implementation.

Maintenance

Publisher activity observed 2026-07-30T19:00:00.000Z

Current source snapshot

Trust review

Source relationship: First-party MCP

Reviewed 2026-07-24 · Next review 2026-09-24

Evidence source 1 ↗

Evidence-backed setup

Install & Agents

Choose your MCP client. A client connects to the MCP server; the language model is selected inside that client. Only stored, source-backed configurations are shown.

Claude Code

Streamable HTTP

MCP100 logic checked

claude mcp add --transport http datadog "https://mcp.datadoghq.com/v1/mcp"

The publisher connection details were combined with this client's official MCP transport configuration; installation was not executed by MCP100.

Verify safely: Reconnect Claude Code, complete the publisher OAuth or authentication flow if prompted, confirm datadog tools are listed, and perform one read-only test.

Uninstall and revoke

Remove datadog from Claude Code, clear its stored MCP OAuth credentials, and revoke the publisher authorization if it is no longer needed.

Evidence & methodology

Publisher-documented setup is not the same as MCP100 execution testing. “MCP100 logic checked” means the stored configuration shape was reviewed, not that third-party MCP software was run.

Evidence checked 2026-07-24

Evidence 1 ↗Evidence 2 ↗

Capability

What it does

Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.

Monitoring & ObservabilityHealthcare & Life SciencesSoftware & Technology

Representative uses

Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.
Teams already using Datadog that can test with non-sensitive data and least privilege.

Important limits

Users who need an MCP100-selected recommendation today
Unattended production workflows before permissions and failure behavior are reviewed

Tools21 documented capabilitiesView tools

Logs, Events & RUM5 capabilities
Investigate operational events, logs, and real-user monitoring data to understand what changed and how users or systems were affected.
Metrics & Service Health6 capabilities
Explore metrics, hosts, services, and service dependencies to understand system health and the relationships behind performance changes.
Traces & Spans2 capabilities
Inspect distributed traces and search spans to follow requests across services and isolate latency or failure paths.
Dashboards & Notebooks5 capabilities
Find monitoring dashboards and work with notebooks that capture investigation context, analysis, and operational findings.
Monitors & Incidents3 capabilities
Find monitors and incidents so active problems can be triaged with the alerting and incident context that surrounds them.

View capability names (21)

search_datadog_events
analyze_datadog_logs
search_datadog_logs
search_datadog_rum_events
aggregate_rum_events
get_datadog_metric
get_datadog_metric_context
search_datadog_hosts
search_datadog_metrics
search_datadog_services
search_datadog_service_dependencies
get_datadog_trace
search_datadog_spans
search_datadog_dashboards
get_datadog_notebook
search_datadog_notebooks
create_datadog_notebook
edit_datadog_notebook
search_datadog_monitors
get_datadog_incident
search_datadog_incidents

Profile: Default `core` toolset The documented default core toolset contains 21 names.

Source: Official documentation

Context Footprint

~4.2K estimated schema tokens

Context impact: Medium

Estimated token footprint of the MCP tool schemas exposed to the agent.

Approx. estimate

Default `core` toolset

Read publisher documentation

Publisher documentation

Official Docs

Original product, capability, tool, and usage documentation from the MCP publisher. Installation sections appear under Install & Agents.

View original documentation ↗

Title: Datadog MCP Server

URL Source: https://docs.datadoghq.com/mcp_server/

Markdown Content: For AI agents: A markdown version of this page is available at https://docs.datadoghq.com/mcp_server.md. A documentation index is available at /llms.txt. PRODUCT CUSTOMERS PRICING SOLUTIONS ABOUT BLOG LOGIN GET STARTED FREE

Datadog Docs

ESSENTIALS

Getting Started Glossary Standard Attributes Guides Agent Integrations Client SDKs Extend Datadog OpenTelemetry Administrator's Guide API Command Line MCP Server Setup MCP Tools Partners Datadog Mobile App DDSQL Reference CoTerm Remote Configuration Cloudcraft (Standalone)

IN THE APP

Dashboards Notebooks DDSQL Editor Reference Tables Sheets Monitors and Alerting Service Level Objectives Metrics Watchdog Bits AI Internal Developer Portal Error Tracking Change Tracking Event Management

INCIDENT RESPONSE

Incident Management On-Call Status Pages Work Management

ACTIONS & REMEDIATIONS

Bits Agent Builder Workflow Automation Apps App Builder Datastores Forms Action Catalog

INFRASTRUCTURE

Cloudcraft Resource Catalog Universal Service Monitoring End User Device Monitoring Hosts Containers Processes Serverless Network Monitoring Storage Management

CLOUD COST

Cloud Cost

APPLICATION PERFORMANCE

APM Continuous Profiler Database Monitoring Data Streams Monitoring

DATA OBSERVABILITY

Data Observability Overview Data Catalog Lineage Quality Monitoring Jobs Monitoring CI/CD Integration Overhead

DIGITAL EXPERIENCE

Journey Monitoring Real User Monitoring Synthetic Testing and Monitoring Continuous Testing Experiments Product Analytics Session Replay

SOFTWARE DELIVERY

CI Visibility CD Visibility Deployment Gates Test Optimization Code Coverage PR Gates Delivery Performance Feature Flags

DEVELOPER INTEGRATIONS

Source Code Integration IDE Plugins

SECURITY

Security Overview Cloud SIEM Code Security Cloud Security App and API Protection AI Guard Workload Protection Sensitive Data Scanner

AI OBSERVABILITY

Agent Observability GPU Monitoring Agent Console

LOG MANAGEMENT

Observability Pipelines Log Management BYOC Logs

ADMINISTRATION

Account Management Disaster Recovery Data Security Help Datadog MCP Server DOCS > DATADOG MCP SERVER

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? Select your agent below or 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 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. The Datadog MCP Server is not GovCloud compatible. 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. Data handling and AI providers

The Datadog MCP Server does not send your Datadog data to a third-party AI provider. Your AI client and its model determine what Datadog data is sent to your AI provider. That data flow is governed by your agreement with that provider, not by Datadog.

What the Datadog MCP Server receives and returns

The MCP Server receives individual tool calls, such as a request to search logs with a given query. It does not receive your prompt or the model’s reasoning, only the tool name and its arguments. The MCP Server returns results to the calling client and makes no outbound calls to external domains. Any web search, webhook, or other external integration you configure in your AI client runs on the client side.

Most MCP Server tools, such as search_datadog_logs, query Datadog backends directly with no AI model involved. A small number of tools do use AI models hosted by Datadog’s AI providers. Examples include tools that perform semantic search or build a query from a natural language description. To disable generative AI providers for your entire organization, contact Datadog support.

Restrict which data the Datadog MCP Server can access

The MCP Server forwards the authenticated user’s own credentials to Datadog APIs. Your existing access controls apply exactly as they do for direct API or UI access. The MCP Server cannot grant a user access beyond what that user already has. It cannot reach resources that are not visible to that user in the Datadog UI.

Because your AI client controls what it sends to its model provider, limiting what a provider can receive means limiting what the MCP Server returns. To scope the data an MCP Server user can retrieve, use:

Role-based access control (RBAC) to grant permissions by role. Data Access Control to restrict which users can read sensitive data, such as logs or APM spans. Log restriction queries to limit a role’s log access to the subset of logs matching a query.

Write operations require the corresponding permission, such as monitors_write, and the MCP Server checks it on each tool call. A read-only user’s call to a write-enabled tool is rejected.

Fair-use rate limits

The MCP Server comes with the following fair-use limits:

50 requests/10 seconds tool call burst limits 100,000 monthly tool calls.

These limits are subject to change and can be adjusted if your use case requires more. Please contact Datadog support for requests or questions.

Monitoring the Datadog MCP Server tool calls

You can track Datadog MCP Server usage for your organization using Datadog metrics and Audit Trail.

All tool calls are recorded in the Datadog Audit Trail with metadata identifying them as MCP actions, including the tool name, arguments, user identity, and the MCP client used. See Track tool calls in Audit Trail for more information.

Datadog also emits two standard metrics that you can use to monitor MCP Server activity:

datadog.mcp.session.starts: Emitted on each session initialization. datadog.mcp.tool.usage: A distribution metric emitted on each tool call.

Both metrics are tagged with attributes such as user_id, user_email, client (the MCP client name, such as claude or cursor), and tool_name.

Because datadog.mcp.tool.usage is a distribution metric, use count (not sum) with .as_count() to get the number of tool calls. For example, to query the total number of tool calls grouped by user email:

count:datadog.mcp.tool.usage{*} by {user_email}.as_count()

Available tools

See Datadog MCP Server Tools for a complete reference of available tools organized by toolset, with example prompts. To enable specific toolsets, see Set Up the Datadog MCP Server for instructions.

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. You can limit available tools at connection time with toolsets and omit_tools. See Set Up the Datadog MCP Server. 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. Use this feedback form to share any feedback, use cases, or issues encountered with your prompts and queries.

FAQ

What can Datadog MCP investigate?

MCP100's current stored description says: Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.

Can Datadog MCP query logs, metrics, traces, and monitors?

MCP100's current stored permissions review says: Exact permissions and the read/write boundary have not yet completed MCP100 independent review; consult the linked publisher sources and use the narrowest available scope.

Does Datadog MCP work with Claude Code?

Yes. MCP100 found MCP100 logic checked setup for Claude Code. The current reviewed evidence also documents setup for Claude Desktop, Codex, VS Code, Cursor, and OpenCode. This reflects setup evidence, not an MCP100 execution test or a guarantee that a current client will connect.

What sensitive observability data can an AI agent access through Datadog MCP?

MCP100's current stored data-handling review says: Publisher-accessible account or application data may be returned to the MCP client and its selected model. Retention and subprocess behavior require candidate-specific review.

Is this an official MCP?

Datadog is listed as First-party MCP in MCP100’s current evidence.

Current official resources:

Publisher documentation ↗

Title: Datadog MCP Server

URL Source: https://docs.datadoghq.com/mcp_server/

Datadog Docs

ESSENTIALS

IN THE APP

INCIDENT RESPONSE

Incident Management On-Call Status Pages Work Management

ACTIONS & REMEDIATIONS

Bits Agent Builder Workflow Automation Apps App Builder Datastores Forms Action Catalog

INFRASTRUCTURE

Cloudcraft Resource Catalog Universal Service Monitoring End User Device Monitoring Hosts Containers Processes Serverless Network Monitoring Storage Management

CLOUD COST

Cloud Cost

APPLICATION PERFORMANCE

APM Continuous Profiler Database Monitoring Data Streams Monitoring

DATA OBSERVABILITY

Data Observability Overview Data Catalog Lineage Quality Monitoring Jobs Monitoring CI/CD Integration Overhead

DIGITAL EXPERIENCE

Journey Monitoring Real User Monitoring Synthetic Testing and Monitoring Continuous Testing Experiments Product Analytics Session Replay

SOFTWARE DELIVERY

CI Visibility CD Visibility Deployment Gates Test Optimization Code Coverage PR Gates Delivery Performance Feature Flags

DEVELOPER INTEGRATIONS

Source Code Integration IDE Plugins

SECURITY

Security Overview Cloud SIEM Code Security Cloud Security App and API Protection AI Guard Workload Protection Sensitive Data Scanner

AI OBSERVABILITY

Agent Observability GPU Monitoring Agent Console

LOG MANAGEMENT

Observability Pipelines Log Management BYOC Logs

ADMINISTRATION

Account Management Disaster Recovery Data Security Help Datadog MCP Server DOCS > DATADOG MCP SERVER

Ready to get started? Select your agent below or 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):

What the Datadog MCP Server receives and returns

Restrict which data the Datadog MCP Server can access

Write operations require the corresponding permission, such as monitors_write, and the MCP Server checks it on each tool call. A read-only user’s call to a write-enabled tool is rejected.

Fair-use rate limits

The MCP Server comes with the following fair-use limits:

50 requests/10 seconds tool call burst limits 100,000 monthly tool calls.

These limits are subject to change and can be adjusted if your use case requires more. Please contact Datadog support for requests or questions.

Monitoring the Datadog MCP Server tool calls

You can track Datadog MCP Server usage for your organization using Datadog metrics and Audit Trail.

Datadog also emits two standard metrics that you can use to monitor MCP Server activity:

datadog.mcp.session.starts: Emitted on each session initialization. datadog.mcp.tool.usage: A distribution metric emitted on each tool call.

Both metrics are tagged with attributes such as user_id, user_email, client (the MCP client name, such as claude or cursor), and tool_name.

count:datadog.mcp.tool.usage{*} by {user_email}.as_count()

Available tools

See Datadog MCP Server Tools for a complete reference of available tools organized by toolset, with example prompts. To enable specific toolsets, see Set Up the Datadog MCP Server for instructions.

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:

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. Use this feedback form to share any feedback, use cases, or issues encountered with your prompts and queries.

Datadog MCP

Investigate Datadog logs, metrics, traces, monitors, dashboards, and security signals.

Best for

Not ideal for

Avoid if

Trade-offs

Evidence

Popularity

Maintenance

Trust review

Install & Agents

Claude Code

What it does

Representative uses

Important limits

Context Footprint

Access & risk

Other options

Grafana Cloud MCP

MCP Server for Splunk platform

Google MCP Toolbox for Databases

Official Docs

FAQ

Official Docs