Detect the language of a text with a confidence score. Pure CPU, instant.
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_lang"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyse |
Changes observed during successful MCP inspections.
Input schema / properties / textAdded value: +{
+ "description": "Text to analyse",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "text"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral context; it does disclose the confidence-score return and that processing is CPU-based and fast. It omits supported languages, input-size limits, mixed-language handling, and output format, so meaningful gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with the core action and return front-loaded and no redundant wording. The performance note is brief and relevant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter utility, purpose and a rough return characteristic are covered, but the absence of sibling differentiation and any input constraints leaves the agent to infer too much. Given the existing detect_language sibling, this is not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the sole parameter is already documented as 'Text to analyse'; the description adds no input semantics such as length limits or language constraints. Baseline 3 is appropriate when the schema does the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb-resource pair ('Detect the language of a text') and adds that it returns a confidence score. It does not distinguish itself from the near-identical sibling detect_language, so it falls short of the top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no when-to-use guidance beyond the implied purpose and does not mention the near-duplicate detect_language sibling or when to prefer either tool. 'Pure CPU, instant' describes performance, not selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.