agents_list
List all AI agents configured in the workspace. Returns agents with their basic info, trigger count, and knowledge collection count. Each agent's description field tells you when that agent is useful. If you're a router-style agent deciding whether to delegate via agent.handoff, read descriptions...
This record as markdown: /tools/io-github-saloprj-dialogbrain/agents-list.md
What agents_list does on Dialogbrain
AI agents call agents_list 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 |
|---|---|---|---|
status | string | — | Filter by status ('active' / 'paused' / 'archived'). Omit for all. |
Parameters from the server's own tool schema.
Why agents_list is rated Low
This tool only retrieves and queries metadata about existing agents in the system. It does not create, modify, delete, or execute any operations — merely reads and filters configuration information. The lowest-severity Read category applies.
From the tool's definition Tool description states 'List all AI agents configured in the workspace' and 'Returns agents with their basic info, trigger count, and knowledge collection count.' The use cases are 'See all configured AI agents' and 'Filter by status' — pure retrieval…
Attacks that exploit this kind of access
The rule that runs agents_list 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_list, this is the rule to start with:
agents_list 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_list call is checked against it from then on.
Questions about agents_list
List all AI agents configured in the workspace. Returns agents with their basic info, trigger count, and knowledge collection count. Each agent's description field tells you when that agent is useful. If you're a router-style agent deciding whether to delegate via agent.handoff, read descriptions and pick the best fit. Use this to: - See all configured AI agents - Filter by status (active/paused/archived) - Get agent IDs for further operations. It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.
agents_list accepts 1 parameter: status. 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_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_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_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_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_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.
More on Dialogbrain, and thousands of servers like it.
This server
Across the catalogue