agents_activity
See what you — or another agent in your workspace — actually did over a time window: messages sent, documents created, calls made, plus a summary (run counts, per-day, top tools). Use this to answer 'what did I do today / yesterday / last week / in the last hour?' or 'what did <agent> do?' with r...
This record as markdown: /tools/io-github-saloprj-dialogbrain/agents-activity.md
What agents_activity does on Dialogbrain
AI agents call agents_activity 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 |
|---|---|---|---|
agent | string | — | Target agent: name, slug, or numeric id. OMIT for yourself. |
limit | integer | — | Max actions / recent runs to return. |
since | string | — | Window start — ISO datetime or date. OMIT for last 24h. |
until | string | — | Window end — ISO datetime or date (exclusive day-end for a bare date). OMIT for now. |
Parameters from the server's own tool schema.
Why agents_activity is rated Low
This tool retrieves historical activity data about agents — it reads and returns logs/summaries without creating, modifying, or deleting anything. Severity is medium because it exposes potentially sensitive behavioral/operational data about agents and their actions in a shared workspace, which could be misused for reconnaissance or privacy violations.
From the tool's definition 'See what you — or another agent in your workspace — actually did over a time window: messages sent, documents created, calls made, plus a summary (run counts, per-day, top tools)'
Attacks that exploit this kind of access
The rule that runs agents_activity 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_activity, this is the rule to start with:
agents_activity 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_activity call is checked against it from then on.
Questions about agents_activity
See what you — or another agent in your workspace — actually did over a time window: messages sent, documents created, calls made, plus a summary (run counts, per-day, top tools). Use this to answer 'what did I do today / yesterday / last week / in the last hour?' or 'what did <agent> do?' with real data instead of guessing. Omit agent for your own activity, or pass another workspace agent's name, slug, or id. Pass since/until as ISO datetimes (e.g. '2026-06-03T09:00:00') for sub-day windows like the last hour, or plain dates ('2026-06-03') for whole days — compute them from the current date/time you were given. Defaults to the last 24h. Traces are retained 30 days. Times are interpreted as UTC — if the current time you were given is in another timezone, convert to UTC before passing since/until. It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.
agents_activity accepts 4 parameters: agent, limit, since, until. 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_activity: 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_activity 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_activity 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_activity. 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_activity 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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