Tanod MCP Server (Deprecated)

by dev.tanod

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This connector has been deprecated

The correct URL is https://tanod.dev/mcp and NOT https://tanod.dev/mcp/ (CORRECT: NO TRAILING SLASH)

Correct Glama listing: https://glama.ai/mcp/connectors/dev.tanod/tanod

WRONG Glama listing: https://glama.ai/mcp/connectors/dev.tanod/tanod-mcp-server/

utilpeek: detect the language of a text

detect_language

Read-onlyIdempotent

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

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

Output 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.

Changed1 schema field changedNov 3, 2026
- addedOutput schema / properties / source / properties / url
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
First observedOct 22, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes far beyond the readOnly/idempotent annotations: it names the return payload (language code in two standards, name, confidence, 3 alternatives), defines the `reliable` threshold (>=20 letters, confidence >=0.6), documents the 422 no_text error and that it is not charged, and discloses pricing, the free daily quota, latency, and that input text is never logged. This is unusually complete behavioral disclosure for a read-only tool.

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?

Front-loads the purpose and the sibling redirect before the operational details, and every sentence carries information an agent could act on. It is dense and packed into one long paragraph, with license attributions for lingua and langid.py that are the least actionable content.

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 single-parameter, read-only detection tool with an output schema, everything needed to call it correctly is present: scope, input constraints, reliability semantics, error behavior, cost, and latency. Nothing material is left to inference.

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 meaning the schema lacks: text with no letters returns a 422 no_text, and reliability depends on having at least 20 letters. That gives the agent input-shaping guidance missing from the `text` field.

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 and resource ("Detect the language of a text") and immediately distinguishes itself from the closest sibling by naming translate_text as the alternative. An agent can tell exactly what this does without opening the schema.

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?

Explicitly redirects translation work to translate_text and warns that short text is often flagged unreliable, which shapes when the result should be trusted. It does not spell out positive use cases (e.g., pre-translation routing), but the routing guidance is clear.

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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