Add a document to user
AI agents use docs_add_favorite to create or update resources in Docs MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Docs MCP Server environment.
This tool creates a new association (favorite) between a user and a document. It is a reversible write operation with minimal blast radius — it does not modify document content, delete anything, or have financial implications. Confidence is slightly reduced because the description is truncated and incomplete.
From the tool's definition 'Add a document to user' — adds a favorite/bookmark entry for a document
Attacks that exploit this kind of access
Add a document to user. It is categorised as a Write tool in the Docs MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Docs MCP Server MCP server in PolicyLayer and add a rule for docs_add_favorite: 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 Docs MCP Server. Nothing to install.
docs_add_favorite 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 docs_add_favorite 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 docs_add_favorite. 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.
docs_add_favorite is provided by the Docs MCP Server MCP server (nic01asfr/docs-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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