source_languagesSupported source languages.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes | List of supported source languages |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 source languages",
+ "items": {
+ "properties": {
+ "language": {
+ "description": "Language code",
+ "type": "string"
+ },
+ "name": {
+ "description": "Language name",
+ "type": "string"
+ }
+ },
+ "required": [
+ "language",
+ "name"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety traits. The description adds minimal behavioral context ('supported' implies a canonical set), but does not disclose any additional traits such as pagination, static vs dynamic data, or relationship to other tools. With annotations present, the bar is lower, and this meets a baseline level.
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 an extremely short, front-loaded phrase with no wasted words. Every word contributes to the meaning, making it a model of 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?
Given the tool's simplicity (0 params, output schema present, annotations present), the description is minimally adequate. However, it lacks any mention of how this tool relates to 'target_languages' or 'translate', and does not clarify whether the list is static or dynamic. An agent might need more context 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 there is no parameter documentation burden. According to the rubric, a baseline of 4 is appropriate when there are no parameters, as the description cannot add value beyond that point.
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 'Supported source languages' is a noun phrase rather than a sentence with a verb, making it vague about what the tool does (list? get? set?). It adds the word 'supported' to the title, which slightly clarifies that it returns a set of allowable languages, but it does not explicitly state an operation or differentiate itself from the sibling tool 'target_languages' beyond the word 'source'.
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 description provides no guidance on when to use this tool versus alternatives like 'target_languages' or 'translate'. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.
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.