List all models available in the local Ollama instance. Use this to check what models are loaded and available for delegation.
AI agents call local_models to retrieve information from Mcp Ollama without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves information about available models without modifying, executing against, or affecting any data or systems. It is a straightforward informational query, analogous to a 'list' or 'get' operation. The blast radius of misuse is minimal—an attacker learns what models are available but cannot cause harm through this tool alone.
From the tool's definition Tool name 'local_models' and description 'List all models available in the local Ollama instance' indicate a query/retrieval operation with no side effects.
Attacks that exploit this kind of access
List all models available in the local Ollama instance. Use this to check what models are loaded and available for delegation. It is categorised as a Read tool in the Mcp Ollama MCP Server, which means it retrieves data without modifying state.
Register the Mcp Ollama MCP server in PolicyLayer and add a rule for local_models: 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 Mcp Ollama. Nothing to install.
local_models 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 local_models 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 local_models. 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.
local_models is provided by the Mcp Ollama MCP server (true-alter/mcp-ollama). 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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