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MCP Datadog Server

Datadog

80·Strong

Overall Trust Score

Official Datadog MCP server for observability and monitoring integration. Enables AI models to query metrics, logs, traces, dashboards, and alerts. Includes infrastructure monitoring, APM, and security monitoring. Essential for AI-powered observability, performance optimization, and incident management workflows.

monitoring
observability
mcp
model-context-protocol
Version: 1.0.0
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

88
metrics query reliability
92
Methodology
Query success rate testing
Evidence
Datadog API
Highly reliable metrics and time-series queries
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
log search accuracy
90
Methodology
Search accuracy testing
Evidence
Datadog Log Management
Powerful log search with indexing and analytics
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
real time monitoring
90
Methodology
Real-time monitoring testing
Evidence
Datadog Monitors
Real-time alerting and monitoring with low latency
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
rate limit handling
82
Methodology
Rate limiting behavior testing
Evidence
Datadog Rate Limits
Subject to API rate limits with per-endpoint quotas
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error recovery
86
Methodology
Error handling testing
Evidence
Implementation Review
Handles API errors with exponential backoff and retry
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Security

73
authentication security
85
Methodology
Authentication mechanism review
Evidence
Datadog Authentication
Uses API keys and application keys with scoped permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
api key exposure risk
65
Methodology
Credential security analysis
Evidence
MCP Security Model
Datadog API keys stored locally; AI can access monitoring data
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
infrastructure visibility risk
62
Methodology
Visibility risk assessment
Evidence
Security Analysis
AI can access detailed infrastructure topology and configurations
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
monitor modification control
75
Methodology
Modification control testing
Evidence
Datadog API
Can create, modify, and mute monitors within permissions
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
organization access control
80
Methodology
Access control testing
Evidence
Datadog RBAC
Respects Datadog RBAC and team permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
audit logging
88
Methodology
Audit logging review
Evidence
Datadog Audit Trail
Comprehensive audit logging for all API operations
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

68
metrics data exposure
70
Methodology
Data flow analysis
Evidence
MCP Data Flow
Metrics, logs, and trace data sent to LLM provider
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
log data privacy
62
Methodology
Log privacy assessment
Evidence
Privacy Analysis
Application logs may contain PII, credentials, and sensitive data
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
infrastructure metadata privacy
68
Methodology
Infrastructure privacy assessment
Evidence
Privacy Analysis
Infrastructure topology and host information may be exposed
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
third party data sharing
70
Methodology
Data sharing analysis
Evidence
LLM Provider Policies
Monitoring data shared with LLM provider per their privacy policy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
trace data privacy
72
Methodology
Trace privacy assessment
Evidence
Datadog APM
Distributed traces may contain request parameters and user identifiers
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

86
documentation quality
90
Methodology
Documentation completeness review
Evidence
Datadog MCP Docs
Comprehensive documentation from official Datadog team
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
operation visibility
88
Methodology
Logging and traceability assessment
Evidence
Datadog Audit Trail
All API operations logged in Datadog audit trail and MCP logs
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
open source transparency
92
Methodology
Source code review
Evidence
GitHub Repository
Open source implementation from Datadog
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
api coverage clarity
75
Methodology
API documentation review
Evidence
MCP Server Documentation
Clear documentation of supported Datadog operations
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

87
ease of setup
85
Methodology
Setup complexity assessment
Evidence
Setup Documentation
Simple setup requiring Datadog API and application keys
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
api performance
88
Methodology
Performance benchmarking
Evidence
Datadog API Performance
Fast API responses with global infrastructure (typically 100-500ms)
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
reliability
92
Methodology
Reliability analysis
Evidence
Datadog Infrastructure
Built on highly reliable Datadog infrastructure with 99.9%+ uptime
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
operation coverage
85
Methodology
Feature coverage assessment
Evidence
Datadog MCP Server
Covers metrics, logs, traces, dashboards, monitors, and infrastructure
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
official support
90
Methodology
Maintainer support assessment
Evidence
Datadog Team
Officially maintained by Datadog with active support
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

✨ Strengths

  • •Comprehensive observability platform (metrics, logs, traces, infrastructure)
  • •Official Datadog implementation with active support
  • •Highly reliable with 99.9%+ uptime on global infrastructure
  • •Excellent for AI-powered performance optimization and incident management
  • •Real-time alerting and monitoring with low latency
  • •Comprehensive audit logging for all operations

⚠️ Limitations

  • •Metrics, logs, and trace data exposed to LLM provider
  • •Application logs may contain PII, credentials, and sensitive data
  • •Infrastructure topology and host information may be revealed
  • •Distributed traces may contain request parameters and user identifiers
  • •Subject to Datadog API rate limits
  • •Requires careful log scrubbing to avoid sensitive data exposure

📊 Metadata

license: MIT
supported platforms:
0: All platforms with Node.js/Python
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/DataDog/datadog-mcp-server
api dependency: Datadog REST API
authentication: Datadog API Key and Application Key
first release: 2024-11
maintained by: Datadog

Use Case Ratings

code generation

82

Good for generating monitoring configurations and alert definitions

customer support

78

Useful for investigating customer-reported performance issues

content creation

50

Limited applicability for content workflows

data analysis

95

Excellent for infrastructure analytics, performance analysis, and trend detection

research assistant

75

Useful for researching system behavior and performance patterns

legal compliance

60

Risk of exposing infrastructure details and log data

healthcare

55

Risk of exposing PHI in application logs and traces

financial analysis

65

Moderate risk for financial infrastructure monitoring

education

85

Excellent for teaching observability and monitoring practices

creative writing

42

Low relevance to creative writing workflows