glossary_language_pairsSupported glossary language pairs.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| supported_languages | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / properties / countRemoved value: -{
- "description": "Number of items returned.",
- "type": "integer"
-}Output schema / properties / itemsRemoved value: -{
- "description": "List of supported glossary language pairs",
- "items": {
- "properties": {
- "source_lang": {
- "description": "Source language code",
- "type": "string"
- },
- "target_lang": {
- "description": "Target language code",
- "type": "string"
- }
- },
- "required": [
- "source_lang",
- "target_lang"
- ],
- "type": "object"
- },
- "type": "array"
-}Output schema / properties / supported_languagesAdded value: +{
+ "items": {
+ "properties": {
+ "source_lang": {
+ "type": "string"
+ },
+ "target_lang": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+}Output schema / requiredRemoved value: -[
- "items",
- "count"
-]Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "description": "List of supported glossary language pairs",
+ "items": {
+ "properties": {
+ "source_lang": {
+ "description": "Source language code",
+ "type": "string"
+ },
+ "target_lang": {
+ "description": "Target language code",
+ "type": "string"
+ }
+ },
+ "required": [
+ "source_lang",
+ "target_lang"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is clear. The description adds only the word 'supported', implying a constraint, but does not disclose whether it returns all pairs or how pairs are structured. It neither contradicts nor significantly enriches the annotations.
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?
The description is extremely concise, using only five words. It avoids waste and is easy to parse, though it is arguably too terse to convey full meaning.
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?
Despite having an output schema and strong annotations, the description provides almost no context. It does not clarify the tool's role in glossary management, what a 'pair' means, or how it relates to sibling tools, leaving the agent without enough information to confidently select it.
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?
The tool has zero parameters, so the description carries no parameter burden. Baseline 4 is appropriate for parameterless tools, even though the description adds no parameter-specific information.
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 names the resource 'glossary language pairs' but lacks an explicit verb like 'list' or 'return', making the action ambiguous. It does not distinguish itself from sibling tools such as source_languages or target_languages.
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?
No guidance is given on when to use this tool versus alternatives. It does not mention use cases, context, or relationship to similar tools like source_languages or target_languages.
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.
Many tools have overlapping functionality, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, which are essentially the same with minor differences. The polymarket_* family also has five tools with similar names and purposes, making it easy to select the wrong one despite detailed descriptions.
Tool names mix verb-first patterns (ask, generate, list, remember) with noun-first patterns (entity_profile, polymarket_arbitrage), and include camelCase like ai_visibility_check. This inconsistent naming style makes the set feel arbitrary and harder to navigate.
With 36 tools, the server exceeds the typical well-scoped range of 3-15. While the multi-purpose nature justifies a larger set, the presence of many near-duplicates (beta/grounded variants, multiple polymarket tools) inflates the count without proportional functional gain.
The toolset covers a wide array of domains including translation, entity resolution, financial data, prediction markets, memory, subscriptions, and AI visibility. It appears very comprehensive for its intended multi-purpose server, with no obvious major gaps in core capabilities.