random_quoteFetch a single random inspirational quote (author + text) from ZenQuotes; falls back to dummyjson automatically if ZenQuotes is unavailable. Response includes _source field when served from fallback.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| quote | Yes | The inspirational quote text | |
| author | Yes | The author of the quote |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "author": {
+ "description": "The author of the quote",
+ "type": "string"
+ },
+ "quote": {
+ "description": "The inspirational quote text",
+ "type": "string"
+ }
+ },
+ "required": [
+ "quote",
+ "author"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {}
-]Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, non-destructive, and idempotent. The description adds valuable behavioral context: automatic fallback to dummyjson when ZenQuotes is unavailable, and the inclusion of a _source field in fallback responses, which is beyond what annotations provide.
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 a single sentence, front-loaded with the core purpose ('Fetch a single random inspirational quote') and efficiently adds fallback and response field details without redundant wording.
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, no-parameter tool with an output schema and sufficient annotations, the description covers the purpose, source, fallback behavior, and response format. It is complete given the tool's low complexity.
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 schema is empty and coverage is 100%. The description doesn't need to explain parameters; it adds value by clarifying the output includes author and text and the fallback behavior, which is appropriate for the no-parameter baseline.
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 clearly states the tool fetches a single random inspirational quote with author and text from ZenQuotes. It distinguishes itself from siblings like list_quotes and today_quote through the 'single random' qualifier, though it doesn't explicitly name alternative tools.
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 explicit guidance on when to use this tool versus alternatives. It implies usage through the verb 'fetch' and the 'single random' qualifier, but doesn't state when not to use it or mention alternative tools like list_quotes or today_quote.
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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