Can I run this server remotely or only locally?

You can run it locally using stdio transport or remotely as an HTTP server with configurable host and port.

Does the server support write operations?

Yes, it supports creating and updating monitors, but a read-only mode can disable all write operations.

How do I configure the Datadog site or region?

Set the DD_SITE environment variable (e.g., datadoghq.com or datadoghq.eu) or use the --site CLI flag.

Are all Datadog tools supported?

Most observability tools are supported, including monitors, dashboards, logs, metrics, traces, events, incidents, SLOs, synthetics, and more; some write operations like log index creation are UI-only.

Datadog MCP Server

datadogmonitoringlogsmetricstraces

MCP server exposing Datadog observability APIs for AI agents with logs, metrics, traces, monitors, dashboards, and more.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "tantiope-datadog-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "datadog-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This MCP server provides AI assistants comprehensive access to Datadog observability data and management APIs. It supports querying logs with grep-like syntax, filtering APM traces by duration, status, or errors, and cross-correlating logs, traces, and metrics. The server supports both stdio and HTTP transports, enabling local or remote deployment scenarios. Use it to integrate Datadog monitoring, alerting, and observability data into AI workflows or tools.

Use cases

Search and filter Datadog logs with complex queries

Query and analyze APM traces by status, duration, and errors

List and manage monitors, dashboards, and alerts

Retrieve and correlate metrics, events, and incidents

Inspect log processing pipelines, indexes, and archives

Key features

Grep-like log search with filters

APM trace filtering with duration/status/error queries

Smart sampling modes for token efficiency

Cross-correlation of logs, traces, and metrics

Supports stdio and HTTP transport modes

Extensive coverage of Datadog observability tools

Capabilities & Tool Schemas (22)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Datadog MCP Server.

monitors

List monitors with optional filters

dashboards

List all dashboards

logs

Search logs with query syntax and filters

logs_pipelines

Inspect log processing pipelines and their processors

logs_indexes

Inspect indexes (filter, retention, Flex tier, exclusion filters); `create`/`delete` are UI-only per Datadog and not exposed

logs_archives

Inspect log archives (S3 / GCS / Azure destinations); per-provider credential fields are forwarded unchanged

Documentation Overview

Datadog MCP Server

DISCLAIMER: This is a community-maintained project and is not officially affiliated with, endorsed by, or supported by Datadog, Inc. This MCP server utilizes the Datadog API but is developed independently.

MCP server providing AI assistants with full Datadog observability access. Features grep-like log search, APM trace filtering with duration/status/error queries, smart sampling modes for token efficiency, and cross-correlation between logs, traces, and metrics. Supports both stdio (local) and http (remote/Kubernetes) transports.

Quick Start

Minimal Claude Desktop / VS Code / Cursor config — just the two required keys:

config.json

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": ["-y", "datadog-mcp"],
      "env": {
        "DD_API_KEY": "your-api-key",
        "DD_APP_KEY": "your-app-key"
      }
    }
  }
}

With optional tuning (EU site, custom default limits, longer log windows):

config.json

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": ["-y", "datadog-mcp"],
      "env": {
        "DD_API_KEY": "your-api-key",
        "DD_APP_KEY": "your-app-key",
        "DD_SITE": "datadoghq.eu",
        "MCP_DEFAULT_LIMIT": "50",
        "MCP_DEFAULT_LOG_LINES": "200",
        "MCP_DEFAULT_METRIC_POINTS": "1000",
        "MCP_DEFAULT_TIME_RANGE": "24"
      }
    }
  }
}

To run as an HTTP server (e.g. inside a container or Kubernetes pod), add transport variables to the same env block:

json

"env": {
  "DD_API_KEY": "your-api-key",
  "DD_APP_KEY": "your-app-key",
  "MCP_TRANSPORT": "http",
  "MCP_PORT": "3000",
  "MCP_HOST": "0.0.0.0"
}

Configuration

Required environment variables

bash

DD_API_KEY=your-api-key
DD_APP_KEY=your-app-key

Optional environment variables

bash

DD_SITE=datadoghq.com  # Default. Use datadoghq.eu for EU, etc.

# Limit defaults (fallbacks when the AI doesn't specify)
MCP_DEFAULT_LIMIT=50              # General tools default limit
MCP_DEFAULT_LOG_LINES=200         # Logs tool default limit
MCP_DEFAULT_METRIC_POINTS=1000    # Metrics timeseries data points
MCP_DEFAULT_TIME_RANGE=24         # Default time range in hours

# Transport (alternative to CLI flags — useful in Kubernetes)
MCP_TRANSPORT=stdio               # stdio | http
MCP_PORT=3000                     # HTTP port
MCP_HOST=0.0.0.0                  # HTTP host

Optional flags

bash

--site=datadoghq.com     # Datadog site (overrides DD_SITE)
--transport=stdio|http   # Transport mode (default: stdio)
--port=3000              # HTTP port when using http transport
--host=0.0.0.0           # HTTP host when using http transport
--read-only              # Block all write operations
--disable-tools=synthetics,rum,security    # Comma-separated list of tools to disable

Transports

Transport	When to use	Endpoints
`stdio` (default)	Local MCP clients — Claude Desktop, Cursor, VS Code	n/a (process stdin/stdout)
`http`	Remote / container / Kubernetes	`POST /mcp` · `GET /mcp` (SSE) · `DELETE /mcp` · `GET /health`

Select with --transport=http or MCP_TRANSPORT=http.

Deployment

Docker

config.json

{
  "mcpServers": {
    "datadog": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "DD_API_KEY",
        "-e", "DD_APP_KEY",
        "-e", "DD_SITE",
        "ghcr.io/tantiope/datadog-mcp"
      ],
      "env": {
        "DD_API_KEY": "your-api-key",
        "DD_APP_KEY": "your-app-key",
        "DD_SITE": "datadoghq.com"
      }
    }
  }
}

Kubernetes

Use environment variables — not container args — for transport configuration:

yaml

env:
  - name: DD_API_KEY
    value: "your-api-key"
  - name: DD_APP_KEY
    value: "your-app-key"
  - name: MCP_TRANSPORT
    value: "http"
  - name: MCP_PORT
    value: "3000"
  - name: MCP_HOST
    value: "0.0.0.0"

Note: Kubernetes args: replaces the entire Dockerfile CMD, causing Node.js to receive the flags instead of your application. Environment variables avoid this issue.

Tools

Tool	Action	Category	Description	Required Scopes
`monitors`	list	Alerting	List monitors with optional filters	`monitors_read`
`monitors`	get	Alerting	Get monitor by ID	`monitors_read`
`monitors`	search	Alerting	Search monitors by query	`monitors_read`
`monitors`	create	Alerting	Create a new monitor; `config` is validated against a typed schema covering documented options (notifyNoData, renotifyInterval, thresholds, …) — unknown keys surface in `warnings`. Pass `dry_run: true` to validate without creating (uses `/api/v1/monitor/validate`, allowed in read-only mode).	`monitors_write`
`monitors`	update	Alerting	Update an existing monitor; same validated schema as `create`; partial configs accepted; validation errors short-circuit before any HTTP call as `EINVALID_MONITOR_CONFIG:`	`monitors_write`
`monitors`	preview	Alerting	Render a monitor template (inline `message` or by `monitor_id`/`id`) with optional `context` of variables and conditionals. Returns `{rendered, variablesUsed, variablesMissing, conditionalsResolved, tagConditionalsResolved}`. Supports Datadog Mustache subset: variable substitution + six documented conditionals (`is_alert`, `is_warning`, `is_no_data`, `is_recovery`, `is_alert_to_warning`, `is_warning_to_alert`) + tag conditionals `{{#is_match "tag" "val"}}`/`{{#is_exact_match "tag" "val"}}` (and `^` negations); `{{#each}}`/partials throw `EUNSUPPORTED_TEMPLATE_SYNTAX`. Read-only.	`monitors_read`
`monitors`	test_notification	Alerting	Known limitation: returns `ENOT_SUPPORTED` — Datadog has no public REST endpoint for triggering a test notification. Documentation pointer in response.	n/a
`monitors`	delete	Alerting	Delete a monitor	`monitors_write`
`monitors`	mute	Alerting	Mute a monitor	`monitors_write`
`monitors`	unmute	Alerting	Unmute a monitor	`monitors_write`
`monitors`	top	Alerting	Top N monitors by alert frequency with real monitor names and context breakdown. WARNING: `total_count` includes renotifies/re-evaluations (Datadog emits a renotify event every `renotify_interval` minutes while Alert). For real fires use `action=history`.	`monitors_read`
`monitors`	history	Alerting	Count and list real state transitions for one monitor over a time window. Filters by `transitionType` (default `["alert","alert recovery"]` — fires+recoveries, excludes renotifies) and optional `group`. Returns `{transitions: [...], count, meta}` where `count` is the number of real transitions (e.g. for one always-Alert burn-rate monitor over 7d: 98 raw events vs 38 real transitions).	`monitors_read`, `events_read`
`dashboards`	list	Visualization	List all dashboards	`dashboards_read`
`dashboards`	get	Visualization	Get dashboard by ID	`dashboards_read`
`dashboards`	create	Visualization	Create a new dashboard	`dashboards_write`
`dashboards`	update	Visualization	Update a dashboard	`dashboards_write`
`dashboards`	delete	Visualization	Delete a dashboard	`dashboards_write`
`logs`	search	Logs	Search logs with query syntax and filters	`logs_read_data`, `logs_read_index_data`
`logs`	aggregate	Logs	Aggregate log data with groupBy	`logs_read_data`
`logs_pipelines`	list, get	Logs Config	Inspect log processing pipelines and their processors	`logs_read_config`
`logs_pipelines`	create, update, delete, reorder	Logs Config	Author pipelines and processor chains	`logs_write_config`
`logs_pipelines`	get_order	Logs Config	Read pipeline evaluation order	`logs_read_config`
`logs_indexes`	list, get	Logs Config	Inspect indexes (filter, retention, Flex tier, exclusion filters); `create`/`delete` are UI-only per Datadog and not exposed	`logs_read_config`
`logs_indexes`	update, reorder	Logs Config	Update index filter/retention/quota and reorder evaluation	`logs_write_config`
`logs_indexes`	get_order	Logs Config	Read index evaluation order	`logs_read_config`
`logs_archives`	list, get	Logs Config	Inspect log archives (S3 / GCS / Azure destinations); per-provider credential fields are forwarded unchanged	`logs_read_archives`
`logs_archives`	create, update, delete, reorder	Logs Config	Manage archive destinations; `destination.type` validated against `s3	gcs
`logs_archives`	get_order	Logs Config	Read archive evaluation order	`logs_read_archives`
`metrics`	query	Metrics	Query timeseries data. Response `meta` includes `rollupRequested` (parsed from `rollup(method, seconds)`, with `methodInferred` flag), `rollupEffective` (interval derived from returned pointlist intervals + deduped `intervalsObserved` for multi-series), and `rollupOverridden: boolean` so callers can detect when Datadog silently downsampled.	`metrics_read`, `timeseries_query`

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

npm downloads

Package downloads in the last 30 days.

Last commit

1d ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Datadog MCP Server

It requires Datadog API and application keys set via DD_API_KEY and DD_APP_KEY environment variables.

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Technical Specs & Signals

CategoryMonitoring

PricingBring your own API key (usage-based cost)

9/10 checks healthy over the last 32d

64Quality signal: Good · 64/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools29/30

Adoption & activity9/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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Scanned 22d ago via OSV.dev · datadog-mcp (npm)

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More in 📊 Monitoring →Best MCP servers for Monitoring & Observability →Alternatives to Datadog MCP Server →Install in Claude Desktop Install in Cursor Install in VS Code

Datadog MCP Server

datadogmonitoringlogsmetricstraces

MCP server exposing Datadog observability APIs for AI agents with logs, metrics, traces, monitors, dashboards, and more.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "tantiope-datadog-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "datadog-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

Use cases

Search and filter Datadog logs with complex queries

Query and analyze APM traces by status, duration, and errors

List and manage monitors, dashboards, and alerts

Retrieve and correlate metrics, events, and incidents

Inspect log processing pipelines, indexes, and archives

Key features

Grep-like log search with filters

APM trace filtering with duration/status/error queries

Smart sampling modes for token efficiency

Cross-correlation of logs, traces, and metrics

Supports stdio and HTTP transport modes

Extensive coverage of Datadog observability tools

Capabilities & Tool Schemas (22)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Datadog MCP Server.

monitors

List monitors with optional filters

dashboards

List all dashboards

logs

Search logs with query syntax and filters

logs_pipelines

Inspect log processing pipelines and their processors

logs_indexes

Inspect indexes (filter, retention, Flex tier, exclusion filters); `create`/`delete` are UI-only per Datadog and not exposed

logs_archives

Inspect log archives (S3 / GCS / Azure destinations); per-provider credential fields are forwarded unchanged

Documentation Overview

Datadog MCP Server

DISCLAIMER: This is a community-maintained project and is not officially affiliated with, endorsed by, or supported by Datadog, Inc. This MCP server utilizes the Datadog API but is developed independently.

Quick Start

Minimal Claude Desktop / VS Code / Cursor config — just the two required keys:

config.json

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": ["-y", "datadog-mcp"],
      "env": {
        "DD_API_KEY": "your-api-key",
        "DD_APP_KEY": "your-app-key"
      }
    }
  }
}

With optional tuning (EU site, custom default limits, longer log windows):

config.json

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": ["-y", "datadog-mcp"],
      "env": {
        "DD_API_KEY": "your-api-key",
        "DD_APP_KEY": "your-app-key",
        "DD_SITE": "datadoghq.eu",
        "MCP_DEFAULT_LIMIT": "50",
        "MCP_DEFAULT_LOG_LINES": "200",
        "MCP_DEFAULT_METRIC_POINTS": "1000",
        "MCP_DEFAULT_TIME_RANGE": "24"
      }
    }
  }
}

To run as an HTTP server (e.g. inside a container or Kubernetes pod), add transport variables to the same env block:

json

"env": {
  "DD_API_KEY": "your-api-key",
  "DD_APP_KEY": "your-app-key",
  "MCP_TRANSPORT": "http",
  "MCP_PORT": "3000",
  "MCP_HOST": "0.0.0.0"
}

Configuration

Required environment variables

bash

DD_API_KEY=your-api-key
DD_APP_KEY=your-app-key

Optional environment variables

bash

DD_SITE=datadoghq.com  # Default. Use datadoghq.eu for EU, etc.

# Limit defaults (fallbacks when the AI doesn't specify)
MCP_DEFAULT_LIMIT=50              # General tools default limit
MCP_DEFAULT_LOG_LINES=200         # Logs tool default limit
MCP_DEFAULT_METRIC_POINTS=1000    # Metrics timeseries data points
MCP_DEFAULT_TIME_RANGE=24         # Default time range in hours

# Transport (alternative to CLI flags — useful in Kubernetes)
MCP_TRANSPORT=stdio               # stdio | http
MCP_PORT=3000                     # HTTP port
MCP_HOST=0.0.0.0                  # HTTP host

Optional flags

bash

--site=datadoghq.com     # Datadog site (overrides DD_SITE)
--transport=stdio|http   # Transport mode (default: stdio)
--port=3000              # HTTP port when using http transport
--host=0.0.0.0           # HTTP host when using http transport
--read-only              # Block all write operations
--disable-tools=synthetics,rum,security    # Comma-separated list of tools to disable

Transports

Transport	When to use	Endpoints
`stdio` (default)	Local MCP clients — Claude Desktop, Cursor, VS Code	n/a (process stdin/stdout)
`http`	Remote / container / Kubernetes	`POST /mcp` · `GET /mcp` (SSE) · `DELETE /mcp` · `GET /health`

Select with --transport=http or MCP_TRANSPORT=http.

Deployment

Docker

config.json

{
  "mcpServers": {
    "datadog": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "DD_API_KEY",
        "-e", "DD_APP_KEY",
        "-e", "DD_SITE",
        "ghcr.io/tantiope/datadog-mcp"
      ],
      "env": {
        "DD_API_KEY": "your-api-key",
        "DD_APP_KEY": "your-app-key",
        "DD_SITE": "datadoghq.com"
      }
    }
  }
}

Kubernetes

Use environment variables — not container args — for transport configuration:

yaml

env:
  - name: DD_API_KEY
    value: "your-api-key"
  - name: DD_APP_KEY
    value: "your-app-key"
  - name: MCP_TRANSPORT
    value: "http"
  - name: MCP_PORT
    value: "3000"
  - name: MCP_HOST
    value: "0.0.0.0"

Note: Kubernetes args: replaces the entire Dockerfile CMD, causing Node.js to receive the flags instead of your application. Environment variables avoid this issue.

Tools

Tool	Action	Category	Description	Required Scopes
`monitors`	list	Alerting	List monitors with optional filters	`monitors_read`
`monitors`	get	Alerting	Get monitor by ID	`monitors_read`
`monitors`	search	Alerting	Search monitors by query	`monitors_read`
`monitors`	create	Alerting	Create a new monitor; `config` is validated against a typed schema covering documented options (notifyNoData, renotifyInterval, thresholds, …) — unknown keys surface in `warnings`. Pass `dry_run: true` to validate without creating (uses `/api/v1/monitor/validate`, allowed in read-only mode).	`monitors_write`
`monitors`	update	Alerting	Update an existing monitor; same validated schema as `create`; partial configs accepted; validation errors short-circuit before any HTTP call as `EINVALID_MONITOR_CONFIG:`	`monitors_write`
`monitors`	preview	Alerting	Render a monitor template (inline `message` or by `monitor_id`/`id`) with optional `context` of variables and conditionals. Returns `{rendered, variablesUsed, variablesMissing, conditionalsResolved, tagConditionalsResolved}`. Supports Datadog Mustache subset: variable substitution + six documented conditionals (`is_alert`, `is_warning`, `is_no_data`, `is_recovery`, `is_alert_to_warning`, `is_warning_to_alert`) + tag conditionals `{{#is_match "tag" "val"}}`/`{{#is_exact_match "tag" "val"}}` (and `^` negations); `{{#each}}`/partials throw `EUNSUPPORTED_TEMPLATE_SYNTAX`. Read-only.	`monitors_read`
`monitors`	test_notification	Alerting	Known limitation: returns `ENOT_SUPPORTED` — Datadog has no public REST endpoint for triggering a test notification. Documentation pointer in response.	n/a
`monitors`	delete	Alerting	Delete a monitor	`monitors_write`
`monitors`	mute	Alerting	Mute a monitor	`monitors_write`
`monitors`	unmute	Alerting	Unmute a monitor	`monitors_write`
`monitors`	top	Alerting	Top N monitors by alert frequency with real monitor names and context breakdown. WARNING: `total_count` includes renotifies/re-evaluations (Datadog emits a renotify event every `renotify_interval` minutes while Alert). For real fires use `action=history`.	`monitors_read`
`monitors`	history	Alerting	Count and list real state transitions for one monitor over a time window. Filters by `transitionType` (default `["alert","alert recovery"]` — fires+recoveries, excludes renotifies) and optional `group`. Returns `{transitions: [...], count, meta}` where `count` is the number of real transitions (e.g. for one always-Alert burn-rate monitor over 7d: 98 raw events vs 38 real transitions).	`monitors_read`, `events_read`
`dashboards`	list	Visualization	List all dashboards	`dashboards_read`
`dashboards`	get	Visualization	Get dashboard by ID	`dashboards_read`
`dashboards`	create	Visualization	Create a new dashboard	`dashboards_write`
`dashboards`	update	Visualization	Update a dashboard	`dashboards_write`
`dashboards`	delete	Visualization	Delete a dashboard	`dashboards_write`
`logs`	search	Logs	Search logs with query syntax and filters	`logs_read_data`, `logs_read_index_data`
`logs`	aggregate	Logs	Aggregate log data with groupBy	`logs_read_data`
`logs_pipelines`	list, get	Logs Config	Inspect log processing pipelines and their processors	`logs_read_config`
`logs_pipelines`	create, update, delete, reorder	Logs Config	Author pipelines and processor chains	`logs_write_config`
`logs_pipelines`	get_order	Logs Config	Read pipeline evaluation order	`logs_read_config`
`logs_indexes`	list, get	Logs Config	Inspect indexes (filter, retention, Flex tier, exclusion filters); `create`/`delete` are UI-only per Datadog and not exposed	`logs_read_config`
`logs_indexes`	update, reorder	Logs Config	Update index filter/retention/quota and reorder evaluation	`logs_write_config`
`logs_indexes`	get_order	Logs Config	Read index evaluation order	`logs_read_config`
`logs_archives`	list, get	Logs Config	Inspect log archives (S3 / GCS / Azure destinations); per-provider credential fields are forwarded unchanged	`logs_read_archives`
`logs_archives`	create, update, delete, reorder	Logs Config	Manage archive destinations; `destination.type` validated against `s3	gcs
`logs_archives`	get_order	Logs Config	Read archive evaluation order	`logs_read_archives`
`metrics`	query	Metrics	Query timeseries data. Response `meta` includes `rollupRequested` (parsed from `rollup(method, seconds)`, with `methodInferred` flag), `rollupEffective` (interval derived from returned pointlist intervals + deduped `intervalsObserved` for multi-series), and `rollupOverridden: boolean` so callers can detect when Datadog silently downsampled.	`metrics_read`, `timeseries_query`

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

npm downloads

Package downloads in the last 30 days.

Last commit

1d ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Datadog MCP Server

It requires Datadog API and application keys set via DD_API_KEY and DD_APP_KEY environment variables.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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Technical Specs & Signals

CategoryMonitoring

PricingBring your own API key (usage-based cost)

9/10 checks healthy over the last 32d

64Quality signal: Good · 64/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools29/30

Adoption & activity9/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 22d ago via OSV.dev · datadog-mcp (npm)

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