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Text Language

TextLanguage
Read-only

Text Language: Text Language is a language detection API that detects the language of any input string, returning the full language name and its two-letter ISO 639-1 code. Send plain text passages or short snippets to identify incoming multilingual content. (Cost: 2 credits)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "TextLanguage"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text to detect the language of

Output Schema

NameRequiredDescriptionDefault
dataNo
errorNoError message, null on success
statusNo"ok" on success

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description's genuinely additive content is the operational cost (2 credits) plus the note that it accepts short snippets; it says nothing about limits such as minimum input length, mixed-language input, or confidence, which matter for a detection 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?

The description is short and front-loads the core capability, with cost correctly placed at the end. It loses a little to redundancy — "Text Language: Text Language is a language detection API" repeats the title twice, and the return format is restated despite an output schema.

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

Completeness3/5

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

For a one-parameter, read-only detector with an output schema, little explanation is required, and the essentials (verb, output fields, cost) are present. The missing piece is sibling differentiation against LanguageDetector, plus any input-length or confidence caveats, leaving an agent with enough to call it but not enough to prefer it over the alternative.

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

Parameters3/5

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

The schema has a single fully-described parameter (100% coverage) with an example, so the schema does the heavy lifting and a 3 is the baseline. The description only loosely adds that "any input string" or a short snippet is acceptable, which is marginal beyond the schema's own wording.

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

Purpose4/5

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

States a specific verb and resource — detects the language of an input string and returns the ISO 639-1 code — so the operation is unambiguous. However, it never distinguishes itself from the near-identical sibling LanguageDetector (or Translator / CountryLanguages), which is exactly the ambiguity a purpose statement should resolve.

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

Usage Guidelines3/5

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

"Send plain text passages or short snippets to identify incoming multilingual content" implies the input shape and a use case, so usage is inferable rather than stated. There is no explicit when-to-use-this-vs-alternative guidance and no mention of LanguageDetector, which an agent would need to choose between the two.

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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