Return the distribution of skill tags across all active operator claims on the TensorFeed Agent Directory. Sorted by count desc. Useful for agents discovering what kinds of work other operators advertise on TF, OR for agent-marketplaces wanting to mirror TF's controlled vocab.
AI agents call list_directory_skills to retrieve information from TensorFeed without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and lists aggregated metadata about skill tag distributions in a directory. It has no side effects, does not execute code, does not modify or delete data, and does not involve financial transactions. It is purely informational/observational, making it a Read category tool with low severity even if misused by an agent (worst case: wasted API quota or noise in data analysis).
From the tool's definition Tool 'returns' and 'discovers' information about skill tag distribution across operator claims; no modification, deletion, or execution of operations. Explicitly supports information retrieval use cases ('Useful for agents discovering...').
Attacks that exploit this kind of access
Return the distribution of skill tags across all active operator claims on the TensorFeed Agent Directory. Sorted by count desc. Useful for agents discovering what kinds of work other operators advertise on TF, OR for agent-marketplaces wanting to mirror TF's controlled vocab. It is categorised as a Read tool in the TensorFeed MCP Server, which means it retrieves data without modifying state.
Register the TensorFeed MCP server in PolicyLayer and add a rule for list_directory_skills: 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 TensorFeed. Nothing to install.
list_directory_skills 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 list_directory_skills 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 list_directory_skills. 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.
list_directory_skills is provided by the TensorFeed MCP server (https://mcp.tensorfeed.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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