Tanod MCP server

mlpeek: semantic similarity of text pairs

text_similarity

Read-onlyIdempotent

Measure semantic similarity for one or up to 50 text pairs by returning embedding cosine similarity scores, useful for matching and duplicate detection.

Instructions

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

Input Schema

TableJSON Schema

Name	Required	Description	Default
`a`	No	First text of a single pair (at most 4,000 characters).
`b`	No	Second text of a single pair.
`model`	No	small-en (English) or multilingual.	small-en
`pairs`	No	Alternative to a / b: 1-50 {a, b} pairs. Priced per pair, minimum USD 0.001 per call.

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`model`	No
`usage`	No
`metric`	No
`results`	No
`similarity`	No
`max_tokens_per_text`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, yet the description adds substantial behavior beyond them: 512-token truncation with a `truncated` flag, a 422 on >16,384 tokens, a 503 for unavailable models (both not charged), latency expectations, pricing, per-IP free quota, and a no-log/no-store privacy guarantee. This is rich disclosure of what actually happens at runtime.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is dense and parenthetical-heavy, but well front-loaded: purpose, alternative, return value, input shape, then limits/cost/privacy in descending priority. Every sentence carries information, though the phrasing could be tightened.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-param scoring tool with an output schema present, the description covers return format, input constraints, model selection, failure modes, cost, and privacy. An agent has everything needed to call it correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds value by mapping the opaque enum values (`small-en`/`multilingual`) to concrete models (bge-small-en, multilingual-e5-small) and clarifying the `pairs` vs `a`/`b` alternative plus its pricing model. This goes beyond the schema's terse field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource ('Score the semantic similarity of one or up to 50 text pairs') and immediately names the sibling it is not ('To rank documents against a query use rerank_documents'). An agent can distinguish it from rerank_documents, embed_texts, and embed_multimodal without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly routes the agent: use this for scoring similarity of text pairs, use rerank_documents to rank documents against a query. It also gives per-call scale guidance (1-50 pairs) and free-tier limits, so the when-to-use decision is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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