What can @winor30/mcp-server-datadog do?

datadog metric query execution via mcp protocol, datadog event creation and search via mcp tools, datadog monitor state retrieval and management via mcp, datadog host and infrastructure inventory retrieval, mcp protocol server implementation for datadog api abstraction, datadog api authentication and credential management, datadog api error handling and response normalization

@winor30/mcp-server-datadog

MCP ServerFree

MCP server for interacting with Datadog API

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

datadog metric query execution via mcp protocol

Medium confidence

Executes metric queries against Datadog's time-series database through MCP tool invocation, translating natural language or structured query parameters into Datadog API calls. Implements MCP's tool-calling interface to expose Datadog's metric query endpoint, handling authentication via API key/app key pairs and returning time-series data with timestamps and aggregated values.

Solves for

Query historical metrics from Datadog without writing direct API codeRetrieve time-series data for specific hosts, services, or custom metricsIntegrate Datadog metric retrieval into LLM-driven monitoring workflowsBuild dashboards or reports by fetching raw metric data programmatically

Best for

DevOps engineers building AI-assisted monitoring dashboards

SRE teams automating incident investigation with LLM agents

Platform teams integrating Datadog into internal AI tools

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Datadog account with metric data available

Limitations

Query complexity limited by Datadog API rate limits (default 300 requests/minute)

No built-in query result caching — repeated queries hit API each time

Time range queries require explicit start/end timestamps; no relative time parsing

What makes it unique

Exposes Datadog metric queries as MCP tools rather than requiring direct REST API calls, enabling LLM agents to query metrics through natural language without SDK boilerplate. Uses MCP's standardized tool schema to abstract Datadog API authentication and response parsing.

vs alternatives

Simpler than building custom Datadog SDK integrations because MCP handles tool registration and invocation; more flexible than static dashboards because queries are dynamic and LLM-driven.

datadog event creation and search via mcp tools

Medium confidence

Creates custom events in Datadog and searches existing events through MCP tool invocation, translating event metadata (title, text, tags, priority) into Datadog API calls. Implements bidirectional event management: writing events for incident tracking or automation markers, and querying events by time range or tag filters to correlate with metrics.

Solves for

Create custom events to mark deployments, config changes, or automation actions in DatadogSearch historical events to correlate with metric anomalies during incident investigationTag events with custom metadata for filtering and correlation in LLM workflowsBuild audit trails of AI-driven actions by logging events to Datadog

Best for

Incident response teams automating event correlation with AI

DevOps platforms logging deployment events for observability

SRE teams building self-healing automation with event markers

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Event title and text fields (required for creation)

Limitations

Event creation requires explicit timestamp; no automatic 'now' inference

Event search limited to 100 results per query; pagination not exposed

Custom event aggregation not supported — only basic tag-based filtering

What makes it unique

Bidirectional event management through MCP tools — both creates and queries events, enabling LLM agents to log their own actions and correlate them with system events. Uses Datadog's event API to maintain a unified audit trail of both infrastructure and AI-driven changes.

vs alternatives

More integrated than manual event creation because LLM agents can autonomously log actions; more queryable than webhook-based event logging because search is built-in.

datadog monitor state retrieval and management via mcp

Medium confidence

Retrieves monitor definitions, current state, and alert status from Datadog through MCP tools, translating monitor IDs or filter criteria into API calls that return monitor configuration and active alerts. Enables LLM agents to inspect which monitors are triggered, their thresholds, and associated metadata without direct API knowledge.

Solves for

Check which monitors are currently alerting to understand active incidentsRetrieve monitor thresholds and conditions for incident triageList all monitors in a specific environment or with specific tagsCorrelate monitor alerts with metric data during root cause analysis

Best for

On-call engineers using AI assistants for incident triage

Automated incident response systems that need monitor context

SRE teams building intelligent alert routing based on monitor metadata

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Monitor IDs or tag filters to query

Limitations

Monitor state is point-in-time; no historical state transitions exposed

Bulk monitor retrieval limited to 1000 monitors per request

Monitor edit/update operations not exposed — read-only in current implementation

What makes it unique

Exposes monitor state as queryable MCP tools, allowing LLM agents to inspect alert conditions and thresholds without parsing Datadog UI or raw API responses. Integrates monitor metadata with metric and event data for holistic incident context.

vs alternatives

More actionable than static alert notifications because LLM agents can query monitor details on-demand; more structured than webhook alerts because monitor definitions are queryable.

datadog host and infrastructure inventory retrieval

Medium confidence

Retrieves host inventory, infrastructure metadata, and system information from Datadog through MCP tools, translating host queries into API calls that return host tags, metrics availability, and system details. Enables LLM agents to understand infrastructure topology and correlate hosts with metrics or alerts.

Solves for

List all hosts in a specific environment or with specific tagsRetrieve host metadata (OS, agent version, installed integrations) for contextCorrelate host information with metric queries for targeted monitoringUnderstand infrastructure topology for incident impact assessment

Best for

SRE teams automating infrastructure discovery for incident response

Platform teams building AI-driven capacity planning tools

DevOps engineers investigating host-level issues with AI assistance

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Datadog agent running on hosts to be discovered

Limitations

Host data is eventually consistent; newly added hosts may not appear immediately

Host list limited to 1000 results per query; no pagination exposed

Custom host attributes not queryable — only standard Datadog tags

What makes it unique

Exposes infrastructure inventory as queryable MCP tools, enabling LLM agents to discover and correlate hosts without manual infrastructure documentation. Integrates host metadata with metric and alert data for end-to-end incident context.

vs alternatives

More dynamic than static inventory files because it queries live Datadog data; more contextual than raw host lists because metadata is enriched with agent status and tags.

mcp protocol server implementation for datadog api abstraction

Medium confidence

Implements a Model Context Protocol (MCP) server that exposes Datadog API capabilities as standardized tools, handling MCP message serialization, authentication token management, and error handling. Routes incoming MCP tool calls to appropriate Datadog API endpoints, manages session state, and returns structured responses compatible with MCP clients (Claude, LLM agents, etc.).

Solves for

Enable any MCP-compatible client to query Datadog without custom integration codeStandardize Datadog API access through MCP's tool-calling interfaceSimplify authentication by centralizing API key management in the MCP serverBuild composable Datadog workflows by chaining MCP tool calls

Best for

Teams standardizing on MCP for tool integration across multiple services

LLM application developers who want Datadog access without SDK boilerplate

Organizations building internal AI platforms with pluggable integrations

Requires

Node.js 16+ runtime

MCP SDK (mcp package)

Datadog API credentials (DD_API_KEY, DD_APP_KEY)

Limitations

MCP server must be running as a separate process; adds deployment complexity

Tool definitions are static at server startup; dynamic tool registration not supported

Error handling relies on MCP error format; Datadog-specific error details may be lost

What makes it unique

Implements MCP server pattern to expose Datadog as a standardized tool interface, abstracting away Datadog API complexity and authentication details. Uses MCP's tool schema to define capabilities declaratively, enabling any MCP client to discover and invoke Datadog operations.

vs alternatives

More portable than direct SDK integration because MCP clients are interchangeable; more maintainable than custom API wrappers because MCP is a standard protocol.

datadog api authentication and credential management

Medium confidence

Manages Datadog API authentication by reading API key and application key from environment variables, constructing authenticated HTTP requests with proper headers, and handling authentication failures gracefully. Implements credential validation at server startup and includes error handling for missing or invalid credentials.

Solves for

Securely pass Datadog credentials to the MCP server without hardcodingValidate credentials at startup to fail fast if authentication is misconfiguredAutomatically include authentication headers in all Datadog API requestsHandle authentication errors and provide clear error messages to clients

Best for

DevOps teams deploying MCP servers in containerized environments

Security-conscious organizations requiring environment-based credential management

Teams using CI/CD pipelines that inject secrets via environment variables

Requires

DD_API_KEY environment variable set with valid Datadog API key

DD_APP_KEY environment variable set with valid Datadog application key

Datadog account with API access enabled

Limitations

Credentials must be set as environment variables; no support for credential files or vaults

No credential rotation mechanism — requires server restart to update credentials

API key and app key stored in process memory; no encryption at rest

What makes it unique

Centralizes Datadog credential management in the MCP server, eliminating the need for clients to handle authentication directly. Uses environment variables for credential injection, enabling secure deployment in containerized and cloud environments.

vs alternatives

More secure than embedding credentials in client code because secrets are managed server-side; more flexible than hardcoded credentials because it supports environment-based configuration.

datadog api error handling and response normalization

Medium confidence

Intercepts Datadog API responses, normalizes error formats into MCP-compatible error messages, and handles rate limiting, authentication failures, and malformed responses. Translates Datadog-specific error codes and messages into structured errors that MCP clients can understand and act upon.

Solves for

Provide clear error messages when Datadog API calls failHandle rate limiting gracefully without crashing the MCP serverDistinguish between client errors (bad query) and server errors (Datadog outage)Enable LLM agents to retry or adjust queries based on specific error types

Best for

Robust MCP server deployments that need fault tolerance

LLM agents that need to handle API failures intelligently

Teams debugging integration issues between MCP clients and Datadog

Requires

HTTP client library with error handling (built into Node.js)

Knowledge of Datadog API error codes and response formats

MCP client capable of handling error messages

Limitations

Error details are limited by Datadog API response format; some context may be lost

No automatic retry logic — clients must implement their own retry strategies

Rate limit headers are parsed but not exposed to clients for backoff calculation

What makes it unique

Normalizes Datadog API errors into MCP error format, abstracting away Datadog-specific error codes and enabling clients to handle failures uniformly. Includes rate limit detection and graceful degradation.

vs alternatives

More robust than direct API calls because errors are normalized and handled consistently; more informative than generic HTTP errors because Datadog context is preserved.

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

Related Artifactssharing capabilities

Artifacts that share capabilities with @winor30/mcp-server-datadog, ranked by overlap. Discovered automatically through the match graph.

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datadog-mcp-server

MCP Server for Datadog API

datadog events creation and querying via mcpdatadog metrics query and retrieval via mcpdatadog monitors and alert rules querying via mcpmcp protocol implementation and tool discovery

4 shared capabilities

MCP Server31

@winor30/mcp-server-datadog

MCP server for interacting with Datadog API

datadog metric query execution via mcp protocoldatadog monitor management and querying via mcpdatadog log search and retrieval via mcpdatadog dashboard and widget querying via mcp

4 shared capabilities

MCP Server46

Datadog MCP Server

Query Datadog metrics, logs, and monitors via MCP.

time-series metric querying with datadog query languagemonitor state and alert rule retrievaldashboard creation and configuration management

3 shared capabilities

MCP Server20

MCPVerse

** - A portal for creating & hosting authenticated MCP servers and connecting to them securely.

mcp server monitoring and observability

1 shared capability

MCP Server36

@dynatrace-oss/dynatrace-mcp-server

Model Context Protocol (MCP) server for Dynatrace

dynatrace api resource exposure via mcp protocol

1 shared capability

MCP Server30

@mcp-use/cli

The mcp-use CLI is a tool for building and deploying MCP servers with support for ChatGPT Apps, Code Mode, OAuth, Notifications, Sampling, Observability and more.

observability and structured logging

1 shared capability

Best For

✓DevOps engineers building AI-assisted monitoring dashboards
✓SRE teams automating incident investigation with LLM agents
✓Platform teams integrating Datadog into internal AI tools
✓Incident response teams automating event correlation with AI
✓DevOps platforms logging deployment events for observability
✓SRE teams building self-healing automation with event markers
✓On-call engineers using AI assistants for incident triage
✓Automated incident response systems that need monitor context

Known Limitations

⚠Query complexity limited by Datadog API rate limits (default 300 requests/minute)
⚠No built-in query result caching — repeated queries hit API each time
⚠Time range queries require explicit start/end timestamps; no relative time parsing
⚠Aggregation functions depend on Datadog metric type; custom rollups not supported
⚠Event creation requires explicit timestamp; no automatic 'now' inference
⚠Event search limited to 100 results per query; pagination not exposed

Requirements

Datadog API key (DD_API_KEY environment variable)Datadog application key (DD_APP_KEY environment variable)Datadog account with metric data availableMCP client compatible with Node.js tool invocationEvent title and text fields (required for creation)MCP client with tool invocation supportMonitor IDs or tag filters to queryMCP client with tool invocation

Input / Output

Accepts: metric query string (e.g., 'avg:system.cpu{host:prod}'), timestamp integers (Unix epoch seconds), aggregation parameters (avg, sum, max, min, count), event title (string, max 100 chars), event text (string, markdown supported), tags array (key:value format), priority enum (low, normal, high), timestamp (Unix epoch seconds), monitor ID (integer), tag filters (key:value format), monitor type filter (metric, log, apm, etc.), host name or hostname pattern, environment filter (prod, staging, dev), MCP tool call messages with JSON parameters, MCP initialization handshake, environment variable strings (DD_API_KEY, DD_APP_KEY), HTTP response status codes from Datadog API, JSON error payloads from Datadog API, HTTP headers (rate limit, retry-after)

Produces: JSON time-series data with timestamp and value pairs, Aggregated metric values with metadata, event ID and confirmation (for creation), JSON array of event objects with metadata (for search), JSON monitor object with definition, thresholds, and current state, Array of monitor objects with alert status, JSON host object with metadata, tags, and metrics, Array of host objects with agent status, MCP tool result messages with JSON data, MCP error messages for failed calls, HTTP Authorization headers for Datadog API requests, Authentication validation status (success/failure), MCP error messages with error code and description, Normalized error objects for client-side handling

UnfragileRank

Adoption49%(30% weight)

Quality16%(25% weight)

Ecosystem40%(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

7 capabilities

Visit @winor30/mcp-server-datadog→

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1.7.0

Version

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MCP server for interacting with Datadog API

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Capabilities7 decomposed

datadog metric query execution via mcp protocol

Medium confidence

Solves for

Best for

DevOps engineers building AI-assisted monitoring dashboards

SRE teams automating incident investigation with LLM agents

Platform teams integrating Datadog into internal AI tools

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Datadog account with metric data available

Limitations

Query complexity limited by Datadog API rate limits (default 300 requests/minute)

No built-in query result caching — repeated queries hit API each time

Time range queries require explicit start/end timestamps; no relative time parsing

What makes it unique

vs alternatives

Simpler than building custom Datadog SDK integrations because MCP handles tool registration and invocation; more flexible than static dashboards because queries are dynamic and LLM-driven.

datadog event creation and search via mcp tools

Medium confidence

Solves for

Best for

Incident response teams automating event correlation with AI

DevOps platforms logging deployment events for observability

SRE teams building self-healing automation with event markers

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Event title and text fields (required for creation)

Limitations

Event creation requires explicit timestamp; no automatic 'now' inference

Event search limited to 100 results per query; pagination not exposed

Custom event aggregation not supported — only basic tag-based filtering

What makes it unique

vs alternatives

More integrated than manual event creation because LLM agents can autonomously log actions; more queryable than webhook-based event logging because search is built-in.

datadog monitor state retrieval and management via mcp

Medium confidence

Solves for

Best for

On-call engineers using AI assistants for incident triage

Automated incident response systems that need monitor context

SRE teams building intelligent alert routing based on monitor metadata

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Monitor IDs or tag filters to query

Limitations

Monitor state is point-in-time; no historical state transitions exposed

Bulk monitor retrieval limited to 1000 monitors per request

Monitor edit/update operations not exposed — read-only in current implementation

What makes it unique

vs alternatives

More actionable than static alert notifications because LLM agents can query monitor details on-demand; more structured than webhook alerts because monitor definitions are queryable.

datadog host and infrastructure inventory retrieval

Medium confidence

Solves for

Best for

SRE teams automating infrastructure discovery for incident response

Platform teams building AI-driven capacity planning tools

DevOps engineers investigating host-level issues with AI assistance

Requires

Datadog API key (DD_API_KEY environment variable)

Datadog application key (DD_APP_KEY environment variable)

Datadog agent running on hosts to be discovered

Limitations

Host data is eventually consistent; newly added hosts may not appear immediately

Host list limited to 1000 results per query; no pagination exposed

Custom host attributes not queryable — only standard Datadog tags

What makes it unique

vs alternatives

More dynamic than static inventory files because it queries live Datadog data; more contextual than raw host lists because metadata is enriched with agent status and tags.

mcp protocol server implementation for datadog api abstraction

Medium confidence

Solves for

Best for

Teams standardizing on MCP for tool integration across multiple services

LLM application developers who want Datadog access without SDK boilerplate

Organizations building internal AI platforms with pluggable integrations

Requires

Node.js 16+ runtime

MCP SDK (mcp package)

Datadog API credentials (DD_API_KEY, DD_APP_KEY)

Limitations

MCP server must be running as a separate process; adds deployment complexity

Tool definitions are static at server startup; dynamic tool registration not supported

Error handling relies on MCP error format; Datadog-specific error details may be lost

What makes it unique

vs alternatives

More portable than direct SDK integration because MCP clients are interchangeable; more maintainable than custom API wrappers because MCP is a standard protocol.

datadog api authentication and credential management

Medium confidence

Solves for

Best for

DevOps teams deploying MCP servers in containerized environments

Security-conscious organizations requiring environment-based credential management

Teams using CI/CD pipelines that inject secrets via environment variables

Requires

DD_API_KEY environment variable set with valid Datadog API key

DD_APP_KEY environment variable set with valid Datadog application key

Datadog account with API access enabled

Limitations

Credentials must be set as environment variables; no support for credential files or vaults

No credential rotation mechanism — requires server restart to update credentials

API key and app key stored in process memory; no encryption at rest

What makes it unique

vs alternatives

More secure than embedding credentials in client code because secrets are managed server-side; more flexible than hardcoded credentials because it supports environment-based configuration.

datadog api error handling and response normalization

Medium confidence

Solves for

Best for

Robust MCP server deployments that need fault tolerance

LLM agents that need to handle API failures intelligently

Teams debugging integration issues between MCP clients and Datadog

Requires

HTTP client library with error handling (built into Node.js)

Knowledge of Datadog API error codes and response formats

MCP client capable of handling error messages

Limitations

Error details are limited by Datadog API response format; some context may be lost

No automatic retry logic — clients must implement their own retry strategies

Rate limit headers are parsed but not exposed to clients for backoff calculation

What makes it unique

vs alternatives

More robust than direct API calls because errors are normalized and handled consistently; more informative than generic HTTP errors because Datadog context is preserved.

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

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@winor30/mcp-server-datadog

Capabilities7 decomposed

datadog metric query execution via mcp protocol

datadog event creation and search via mcp tools

datadog monitor state retrieval and management via mcp

datadog host and infrastructure inventory retrieval

mcp protocol server implementation for datadog api abstraction

datadog api authentication and credential management

datadog api error handling and response normalization

Related Artifactssharing capabilities

datadog-mcp-server

@winor30/mcp-server-datadog

Datadog MCP Server

MCPVerse

@dynatrace-oss/dynatrace-mcp-server

@mcp-use/cli

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

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@winor30/mcp-server-datadog

Capabilities7 decomposed

datadog metric query execution via mcp protocol

datadog event creation and search via mcp tools

datadog monitor state retrieval and management via mcp

datadog host and infrastructure inventory retrieval

mcp protocol server implementation for datadog api abstraction

datadog api authentication and credential management

datadog api error handling and response normalization

Related Artifactssharing capabilities

datadog-mcp-server

@winor30/mcp-server-datadog

Datadog MCP Server

MCPVerse

@dynatrace-oss/dynatrace-mcp-server

@mcp-use/cli

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to @winor30/mcp-server-datadog

Are you the builder of @winor30/mcp-server-datadog?

Get the weekly brief

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