Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single ...
Part of the Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox server.
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AI agents call embedding_similarity to retrieve information from Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though embedding_similarity only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"embedding_similarity": {}
}
}See the full Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox policy for all 139 tools.
These attack patterns abuse exactly the kind of access embedding_similarity gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.. It is categorised as a Read tool in the Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox MCP Server, which means it retrieves data without modifying state.
Register the Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox MCP server in PolicyLayer and add a rule for embedding_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 Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox. Nothing to install.
embedding_similarity 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 embedding_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 embedding_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.
embedding_similarity is provided by the Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox MCP server (ia-qa/api). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 139 Ia Qa Com/mcp llm and RAG testing Dev/QA toolbox tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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