Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.
Part of the Embedding Search server.
Free to start. No card required.
AI agents use compute_similarity to create or modify resources in Embedding Search. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call compute_similarity repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Embedding Search.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"compute_similarity": {
"limits": [
{
"counter": "compute_similarity_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Embedding Search policy for all 3 tools.
These attack patterns abuse exactly the kind of access compute_similarity gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.. It is categorised as a Write tool in the Embedding Search MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Embedding Search MCP server in PolicyLayer and add a rule for compute_similarity: 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 Embedding Search. Nothing to install.
compute_similarity 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 compute_similarity 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 compute_similarity. 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.
compute_similarity is provided by the Embedding Search MCP server (https://api.lazy-mac.com/embedding-search/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 3 Embedding Search tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.