Crypto, web, finance and app data tools

Language Detection

detect_language

Detect the language of a text among 55 languages, with probabilities for each candidate and a reliability flag. Use to route messages, pick a translator or tag content.

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": "detect_language"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 22, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the disclosure burden. It usefully reveals that output includes per-language probabilities and a reliability flag, which hints at confidence handling, but says nothing about input limits, mixed-language or short-text behavior, or latency/cost.

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?

Two tight sentences: the first defines the operation and its output shape, the second gives use cases. Nothing redundant, and the core purpose is front-loaded.

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

Completeness4/5

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

An output schema exists, so return values need not be spelled out, and the description still adds the useful probability/reliability framing. The main gap is the absence of any input constraint guidance for the lone parameter.

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?

There is a single required parameter, 'text', with 0% schema description coverage. The description implies what 'text' is but adds no constraints such as maximum length, empty-string handling, or whether mixed-language input is accepted.

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 gives a specific verb+resource ('Detect the language of a text') plus scope ('among 55 languages') and the shape of the result. No sibling tool in the list performs language detection, so it is trivially distinguishable from all of them.

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?

It names concrete application contexts — routing messages, picking a translator, tagging content — which tells the agent when this tool is the right pick. It does not state exclusions or failure conditions (e.g. very short or mixed-language input), so it stops short of full routing 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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