List recent execution traces for an agent — the same data as /admin/requests, scoped to one agent and readable by an LLM. Use this when an agent call timed out, drafted the wrong response, or you want to know which tool/LLM call burned the latency. Pair with agents.trace_get for full detail on a ...
Risk signalsAdmin/system-level operation
Part of the Dialogbrain server.
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AI agents call agents_traces_list to retrieve information from Dialogbrain without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though agents_traces_list only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"agents_traces_list": {}
}
}See the full Dialogbrain policy for all 157 tools.
These attack patterns abuse exactly the kind of access agents_traces_list gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
List recent execution traces for an agent — the same data as /admin/requests, scoped to one agent and readable by an LLM. Use this when an agent call timed out, drafted the wrong response, or you want to know which tool/LLM call burned the latency. Pair with agents.trace_get for full detail on a specific trace. Filters: status, success, source (single value or comma-separated: agent,voice), date_from/date_to (ISO-8601), pagination via limit/offset. Returns returned_count, dropped_on_page (should be 0 — positive means the backend agent_id predicate let something through), and has_more. Edge case: a raw page of all-dedup-dropped rows yields returned_count=0, has_more=true; re-call with offset += limit.. It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for agents_traces_list: 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 Dialogbrain. Nothing to install.
agents_traces_list 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 agents_traces_list 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 agents_traces_list. 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.
agents_traces_list is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 157 Dialogbrain tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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