Search distributed-tracing spans in Grail via DQL (
AI agents call search_spans to retrieve information from Dynatrace SaaS MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves observability data (distributed-tracing spans) from Dynatrace's Grail data lake using DQL queries. It has no side effects and does not create, modify, or delete data.
From the tool's definition Tool name 'search_spans' and description 'Search distributed-tracing spans in Grail via DQL' indicate a query/retrieval operation without modification.
Attacks that exploit this kind of access
Search distributed-tracing spans in Grail via DQL (. It is categorised as a Read tool in the Dynatrace SaaS MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Dynatrace SaaS MCP Server MCP server in PolicyLayer and add a rule for search_spans: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Dynatrace SaaS MCP Server. Nothing to install.
search_spans is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the search_spans rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for search_spans. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
search_spans is provided by the Dynatrace SaaS MCP Server MCP server (mnmozi/dynatrace-mcp-saas). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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