agents_trace_get
Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after agents.traces_list identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM system_prompt and prompt_messages...
This record as markdown: /tools/io-github-saloprj-dialogbrain/agents-trace-get.md
What agents_trace_get does on Dialogbrain
AI agents call agents_trace_get to retrieve information from Dialogbrain without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
full | boolean | — | Disable all field truncation. Escape hatch for a human operator. OMIT for the standard truncated view. |
agent_id | integer | Yes | Expected agent_id — used for scope validation. Mismatch returns not_found. |
trace_id | string | Yes | Trace identifier returned by agents.traces_list. |
include_llm_bodies | boolean | — | Include system_prompt and prompt_messages in LLM spans. Audited at WARNING level. OMIT to keep them stripped (the default). |
Parameters from the server's own tool schema.
Why agents_trace_get is rated Low
This is fundamentally a retrieval operation that queries execution traces and diagnostic information. However, it operates on agent execution data that may contain sensitive information (LLM prompts, messages, completion texts), which elevates it from low to medium severity. The tool can expose internal system prompts and conversation context that could be sensitive if accessed by an unauthorized agent.
From the tool's definition Tool description states "Fetch the full execution detail" — retrieves trace data, tool executions, events timeline, and LLM call spans. No modifications, deletions, or external operations are performed.
Attacks that exploit this kind of access
The rule that runs agents_trace_get safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For agents_trace_get, this is the rule to start with:
agents_trace_get is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Dialogbrain, apply this rule, and every agents_trace_get call is checked against it from then on.
Questions about agents_trace_get
Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after agents.traces_list identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM system_prompt and prompt_messages are stripped — set include_llm_bodies=true to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set full=true to disable all field truncation. completion_text on failed LLM calls is always returned (capped at 8 KB). It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.
agents_trace_get accepts 4 parameters: full, agent_id, trace_id, include_llm_bodies. Required: agent_id, trace_id. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for agents_trace_get: 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_trace_get 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_trace_get 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_trace_get. 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_trace_get 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.
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