text_similarity
mlpeek: Score the semantic similarity of one or up to 50 text pairs. To rank documents against a query use rerank_documents. Returns the cosine similarity of the embeddings of each text pair (bge-small-en or multilingual-e5-small): one pair as a + b (also a top-level similarity) or 1-50 pairs. In...
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What text_similarity does on Tanod
AI agents call text_similarity to retrieve information from Tanod without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
a | object | — | First text of a single pair (at most 4,000 characters). |
b | object | — | Second text of a single pair. |
model | string | — | small-en (English) or multilingual. |
pairs | object | — | Alternative to a / b: 1-50 {a, b} pairs. Priced per pair, minimum USD 0.001 per call. |
Parameters from the server's own tool schema.
Why text_similarity is rated Low
Even though text_similarity only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs text_similarity safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Tanod, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For text_similarity, this is the rule to start with:
text_similarity is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Tanod, apply this rule, and every text_similarity call is checked against it from then on.
Questions about text_similarity
mlpeek: Score the semantic similarity of one or up to 50 text pairs. To rank documents against a query use rerank_documents. Returns the cosine similarity of the embeddings of each text pair (bge-small-en or multilingual-e5-small): one pair as a + b (also a top-level similarity) or 1-50 pairs. Input: a + b, or pairs; each text at most 4,000 characters; optional model. Each text is cut at the model's 512-token window (truncated says so); over 16,384 tokens per request is a 422 (not charged): split it. Runs an open model pinned by hash on Tanod's own CPU (no LLM, no third-party API); an unavailable model is a 503 (not charged). Typically 20-50 ms for one pair, under 1 s for 50 short pairs. Price: USD 0.0005 per pair, at least USD 0.001 per call (1-50 pairs per call: USD 0.001-0.025). Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/embeddings-rerank-ner-mcp-server.html. It is categorised as a Read tool in the Tanod MCP Server, which means it retrieves data without modifying state.
text_similarity accepts 4 parameters: a, b, model, pairs. The full parameter table on this page comes from the server's own tool schema.
Register the Tanod MCP server in PolicyLayer and add a rule for text_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 Tanod. Nothing to install.
text_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 text_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 text_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.
text_similarity is provided by the Tanod MCP server (https://tanod.dev/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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