detect_languageDetect the language of a text string. Returns an array of detected languages with confidence scores.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text whose language should be detected |
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The input text that was analyzed | |
| detections | Yes | Array of detected languages with confidence scores |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "text": "Hola, ¿cómo estás?"
+ },
+ {
+ "text": "The quick brown fox jumps over the lazy dog"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "detections": {
+ "description": "Array of detected languages with confidence scores",
+ "items": {
+ "properties": {
+ "confidence": {
+ "description": "Confidence score for the detection",
+ "type": "number"
+ },
+ "language": {
+ "description": "Detected language code",
+ "type": "string"
+ }
+ },
+ "required": [
+ "language",
+ "confidence"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "text": {
+ "description": "The input text that was analyzed",
+ "type": "string"
+ }
+ },
+ "required": [
+ "text",
+ "detections"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {
- "text": "Hola, ¿cómo estás?"
- },
- {
- "text": "The quick brown fox jumps over the lazy dog"
- }
-]Output schema / (root)Previous value: -{
- "properties": {
- "detections": {
- "description": "Array of detected languages with confidence scores",
- "items": {
- "properties": {
- "confidence": {
- "description": "Confidence score for the detection",
- "type": "number"
- },
- "language": {
- "description": "Detected language code",
- "type": "string"
- }
- },
- "required": [
- "language",
- "confidence"
- ],
- "type": "object"
- },
- "type": "array"
- },
- "text": {
- "description": "The input text that was analyzed",
- "type": "string"
- }
- },
- "required": [
- "text",
- "detections"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "detections": {
+ "description": "Array of detected languages with confidence scores",
+ "items": {
+ "properties": {
+ "confidence": {
+ "description": "Confidence score for the detection",
+ "type": "number"
+ },
+ "language": {
+ "description": "Detected language code",
+ "type": "string"
+ }
+ },
+ "required": [
+ "language",
+ "confidence"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "text": {
+ "description": "The input text that was analyzed",
+ "type": "string"
+ }
+ },
+ "required": [
+ "text",
+ "detections"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "text": "Hola, ¿cómo estás?"
+ },
+ {
+ "text": "The quick brown fox jumps over the lazy dog"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds the return format (array with confidence scores), which is useful behavioral detail beyond the annotations. No contradictions.
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 sentences, front-loaded with the core action, no unnecessary words. Excellent conciseness.
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 tool with an output schema and strong annotations, the description provides the essential behavior and return information. Nothing more is needed.
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 coverage is 100% with the 'text' parameter described as 'The text whose language should be detected'. The description only repeats this, adding no additional semantic detail, so the baseline 3 applies.
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?
The description uses a specific verb ('Detect') and resource ('language of a text string'), clearly distinguishing it from siblings like translate and list_languages. The scope is unambiguous and matches the tool name.
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?
The context of use is clear: when you have a text and need to identify its language. However, it does not explicitly mention alternatives or exclusions, so it falls short of a 5.
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.
Several groups of tools are hard to tell apart in practice: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and bet_research all route natural-language questions to similar data sources, and the polymarket_* family plus bet_research heavily overlaps. Individual descriptions are detailed, but an agent navigating this surface will frequently struggle to choose the correct entry point.
The naming is readable and mostly snake_case, but conventions are mixed: some tools are verb+noun commands (resolve_entity, validate_claim), some are noun phrases (entity_profile, bet_research), and others use product prefixes inconsistently (ask_pipeworx vs pipeworx_feedback vs polymarket_edges). The polymarket_* cluster is consistent, but no clear pattern holds across the whole server.
34 tools is past the 25-tool threshold and is especially excessive for a server named 'translate', where only three tools relate to translation. Most of the surface belongs to a broad Pipeworx data/analytics/prediction-market platform that would be better split into separate focused servers.
The Pipeworx-related workflows are fairly well-covered: lookup, grounded research, company profiling, prediction-market analysis, subscriptions, and memory all have usable tool clusters. However, the translation domain implied by the server name is thin and references a deepl_translate tool that is not actually exposed, so there is no single domain that feels fully complete.