Datadog MCP Server for OpenAI Agents SDK 11 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Datadog through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Datadog Assistant",
instructions=(
"You help users interact with Datadog. "
"You have access to 11 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Datadog"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Datadog MCP Server
Connect your Datadog account to any AI agent and take full control of your infrastructure monitoring and log management through natural conversation.
The OpenAI Agents SDK auto-discovers all 11 tools from Datadog through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Datadog, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
What you can do
- Metric Auditing — Execute static queries targeting numeric telemetry datastores to resolve specific DDQL metrics objects generated dynamically
- Log Investigation — Perform structural extraction matching target string traces inside Datadog logs to evaluate status boundaries across your apps
- Monitor Management — Discover explicit system rule endpoints bounding configured triggers against alert metrics to verify health states
- Telemetry Extraction — Fetch timestamp arrays natively from numeric logged endpoints to analyze performance trends over specific time intervals
- Log Filtering — Apply ISO boundary mappings to compare logging payloads and identify exactly when errors or bottlenecks occurred
The Datadog MCP Server exposes 11 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Datadog to OpenAI Agents SDK via MCP
Follow these steps to integrate the Datadog MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 11 tools from Datadog
Why Use OpenAI Agents SDK with the Datadog MCP Server
OpenAI Agents SDK provides unique advantages when paired with Datadog through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Datadog + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Datadog MCP Server delivers measurable value.
Automated workflows: build agents that query Datadog, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries Datadog, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Datadog tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Datadog to resolve tickets, look up records, and update statuses without human intervention
Datadog MCP Tools for OpenAI Agents SDK (11)
These 11 tools become available when you connect Datadog to OpenAI Agents SDK via MCP:
get_dashboard
Resolves all widget configurations, template variables, and layout structures for visualization rendering. Get dashboard details
get_monitor
Resolves notification settings, threshold values, and historical status changes for the given monitor ID. Get monitor details
list_dashboards
Returns a list of dashboard identifiers, titles, layout types (timeboard/screenboard), and direct access URLs. List all dashboards
list_downtimes
Returns scope tags, recurring schedules, and current status to identify planned maintenance periods. List scheduled downtimes
list_events
Returns a collection of events including titles, priority levels, and source identifiers (e.g., monitor alerts, deployment events). List events
list_hosts
Returns host metadata including agent version, active tags, and associated cloud provider attributes. List infrastructure hosts
list_monitors
Filters results by operational state (alert, warn, no data, ok) and returns monitor metadata including type, query, and current status. List monitors by state
list_slos
Returns SLO definitions including target percentages, time windows, and current compliance status for monitor-based or metric-based objectives. List Service Level Objectives
mute_monitor
Interacts with the alerting boundary to set temporary silence periods, optionally with an automatic expiration timestamp. Mute a monitor
query_metrics
Resolves time-series data within the specified UNIX timestamp range. Returns metric points, scope tags, and unit metadata for infrastructure and application monitoring. Query time-series metrics
search_logs
Interacts with the log storage boundary to retrieve entries matching the query syntax, including timestamps, status levels, and structured attributes. Search application logs
Example Prompts for Datadog in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Datadog immediately.
"Show me the CPU usage for 'web-server' over the last 30 minutes"
"Find logs with '500 Internal Server Error' from the last hour"
"Are there any active monitors in 'Alert' state?"
Troubleshooting Datadog MCP Server with OpenAI Agents SDK
Common issues when connecting Datadog to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Datadog + OpenAI Agents SDK FAQ
Common questions about integrating Datadog MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Datadog with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Datadog to OpenAI Agents SDK
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.