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.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | ID of the AI agent | |
| in_workspace | No | Run this one call in this workspace id instead of the session's. Nothing is stored; other sessions are not affected. | |
| collection_id | Yes | ID of the collection to assign |
Changes observed during successful MCP inspections.
Input schema / properties / in_workspaceAdded value: +{
+ "description": "Run this one call in this workspace id instead of the session's. Nothing is stored; other sessions are not affected.",
+ "type": "integer"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false, idempotentHint=false). The description adds genuinely useful behavioral context beyond that: assignment automatically changes the agent's knowledge.query scoping. It does not clarify whether repeated/duplicate assignments are additive or error, which the idempotentHint=false makes relevant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with zero filler. The action is front-loaded, and the follow-on sentence explains the consequence rather than restating the action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with full schema coverage, no output schema, and annotations covering safety, the description is nearly complete. Only the semantics of re-assignment (additive vs. replacing, idempotency) are left unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so collection_id, agent_id, and in_workspace are all documented in the schema itself. The description adds no parameter-level detail beyond what the schema provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (assign) and resource (a knowledge collection to an AI agent), which is unambiguous and inherently contrasted with the sibling collections_unassign_agent. An agent can tell exactly what this does without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The second sentence explains the effect (the agent's knowledge.query RAG search becomes scoped to assigned collections), which implies the reason to use it, but there is no explicit when-to-use guidance, no mention of the collections_unassign_agent alternative, and no prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.