No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by winor30 · MCP Server · ★ 143
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-server-datadog safe to install? View the security audit →
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 as part of the Model Context Protocol ecosystem. MCP server for the Datadog API, enabling incident management and more. Features Observability Tools: Provides a mechanism to leverage key Datadog monitoring features, such as incidents, monitors, logs, dashboards, and metrics, through the MCP server. Extensible Design: Designed to easily integrate with additional Datadog APIs, allowing for seamless future feature expansion. Tools Retrieve a list of incidents from Datadog. Inputs: (optional string): Filter p
| Stars | 143 |
| Forks | 76 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 60.8936455520401/100 |
| Open Issues | 27 |
| Last Updated | 2026-06-22 |
| Created | 2025-02-23 |
| Platforms | mcp, node |
| Est. Tokens | ~17k |
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mcp-server-datadog is an open-source mcp server by winor30 with 143 GitHub stars.
mcp-server-datadog is primarily written in TypeScript.
You can find installation instructions and usage details in the mcp-server-datadog GitHub repository at github.com/winor30/mcp-server-datadog. The project has 143 stars and 76 forks, indicating an active community.
mcp-server-datadog is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: