Tanod MCP server

utilpeek: detect the language of a text

detect_language

Read-onlyIdempotent

Identify the language of a text and return ISO 639-1/639-3 codes, name, confidence, and alternatives; flag short or low-confidence text as unreliable.

Instructions

utilpeek: Detect the language of a text. To translate use translate_text. Returns the most likely language (ISO 639-1 and 639-3 code, name, confidence) and 3 alternatives, from an offline ensemble of lingua (Apache-2.0) and langid.py (BSD-2-Clause) over 75 languages; reliable is true only with at least 20 letters and confidence 0.6 or more. Short text is often flagged unreliable; text with no letters is a 422 no_text (not charged). Typically under 0.1 s (a few seconds on the worker's first call). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/language-detection-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	The text (at most 20,000 characters).

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`source`	No
`letters`	No
`language`	No
`reliable`	No
`alternatives`	No
`chars_analyzed`	No
`languages_supported`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations: defines the `reliable` flag threshold (20 letters, confidence >= 0.6), the 422 no_text error and that it is not charged, latency expectations including cold-start, price, free daily quota, and that text is never logged or stored. This is unusually rich behavioral disclosure.

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

Conciseness5/5

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

Front-loaded with purpose and alternative routing, then return shape, then edge cases, then operational facts. Dense but every clause carries actionable information; no filler.

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?

Despite an output schema existing, the description still summarizes the return payload (ISO codes, name, confidence, 3 alternatives), plus error, cost, latency, and privacy behavior. Nothing an agent needs to call this correctly is missing.

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% and the schema already documents the 20,000-character limit. The description adds input semantics the schema lacks: letterless text yields 422 no_text and short text is often flagged unreliable, which helps the agent predict outcomes from the input.

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?

States a specific verb (detect) and resource (language of a text) and explicitly distinguishes itself from the sibling translate_text. An agent can tell instantly what this does and what it is not.

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

Usage Guidelines4/5

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

"To translate use translate_text" gives an explicit alternative for the adjacent use case. It does not state when detection is inappropriate (e.g., very short strings), though the reliability note partially covers that. Clear context, but no explicit when-not guidance.

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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