Generate embeddings for text input. Returns numerical vector representations.
AI agents call ollama_embed to retrieve information from Ollama MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool takes text input and returns numerical vector representations (embeddings). It is a pure read/compute operation with no side effects — it does not modify, delete, or create any persistent data, nor does it execute code or trigger external operations.
From the tool's definition Generate embeddings for text input. Returns numerical vector representations.
Documented attack patterns abuse exactly the kind of access ollama_embed gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Ollama MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for ollama_embed:
{
"version": "1",
"default": "deny",
"tools": {
"ollama_embed": {}
}
}ollama_embed is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Generate embeddings for text input. Returns numerical vector representations. It is categorised as a Read tool in the Ollama MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Ollama MCP Server MCP server in PolicyLayer and add a rule for ollama_embed: 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 Ollama MCP Server. Nothing to install.
ollama_embed 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 ollama_embed 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 ollama_embed. 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.
ollama_embed is provided by the Ollama MCP Server MCP server (rawveg/ollama-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 13 Ollama MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
13 Ollama MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.