Cut AI agent cost and improve accuracy with Code Execution in the Datadog MCP Server

Datadog Code Execution, now generally available through the Datadog MCP Server, enables AI agents to conduct multistep observability investigations by generating and running JavaScript in a sandboxed environment. Rather than sending raw results from each separate query through the model, agents can query Datadog APIs for logs, metrics, traces, and other data sources in parallel, apply control flow, correlate and aggregate results, and return only relevant evidence. In tests across 25 observability tasks and four AI models, Code Execution improved average answer correctness from 74.1% to 89.6%, while reducing input-token use by 73.2% and tool calls by 39.6% compared with Datadog’s Core toolset. The system keeps credentials outside the sandbox, with a trusted MCP service making requests under the caller’s existing permissions and policies. Users can enable the code-exec toolset after connecting the Datadog MCP Server to an AI client to investigate questions spanning multiple observability signals.

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