collections_assign_agent
Assign a knowledge collection to an AI agent. Once assigned, the agent's knowledge.query will automatically scope RAG search to files in its assigned collections.
This record as markdown: /tools/io-github-saloprj-dialogbrain/collections-assign-agent.md
What collections_assign_agent does on Dialogbrain
AI agents use collections_assign_agent to create or update resources in Dialogbrain, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dialogbrain environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
agent_id | integer | Yes | ID of the AI agent |
collection_id | integer | Yes | ID of the collection to assign |
Parameters from the server's own tool schema.
Why collections_assign_agent is rated Medium
This tool modifies agent configuration by establishing a persistent relationship between an agent and a knowledge collection. While not destructive (the assignment is reversible) and not financial, it creates/modifies data state that affects agent behavior.
From the tool's definition The tool 'assigns' a knowledge collection to an agent, which is a configuration modification. The description states that assignment affects automatic scoping of RAG search, indicating persistent state change to agent configuration rather than temporary…
Attacks that exploit this kind of access
The rule that runs collections_assign_agent 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 collections_assign_agent, this is the rule to start with:
collections_assign_agent stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. 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 collections_assign_agent call is checked against it from then on.
Questions about collections_assign_agent
Assign a knowledge collection to an AI agent. Once assigned, the agent's knowledge.query will automatically scope RAG search to files in its assigned collections. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
collections_assign_agent accepts 2 parameters: agent_id, collection_id. Required: agent_id, collection_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 collections_assign_agent: 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.
collections_assign_agent is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the collections_assign_agent 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 collections_assign_agent. 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.
collections_assign_agent 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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