list_quotesFetch a batch of up to 50 random inspirational quotes (author + text each) from ZenQuotes; falls back to dummyjson if ZenQuotes is unavailable. Response includes count, quotes array, and _source when from fallback.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of quotes in the list | |
| quotes | Yes | Array of inspirational quotes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of quotes in the list",
+ "type": "integer"
+ },
+ "quotes": {
+ "description": "Array of inspirational quotes",
+ "items": {
+ "properties": {
+ "author": {
+ "description": "The author of the quote",
+ "type": "string"
+ },
+ "quote": {
+ "description": "The inspirational quote text",
+ "type": "string"
+ }
+ },
+ "required": [
+ "quote",
+ "author"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "quotes"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses fallback behavior (dummyjson if ZenQuotes is unavailable) and the _source field in the response. This adds valuable context beyond the annotations, which only indicate read-only and idempotent behavior.
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 two sentences long, front-loaded with the core action, and includes only essential details. Every sentence adds value: the first states the primary function and source, the second clarifies fallback and response shape.
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?
With zero parameters, a simple read-only operation, and an output schema present, the description covers all necessary facets: what is fetched, from where, fallback behavior, and response structure. It is complete for this tool's 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 baseline is 4. The description adds no parameter-specific details because none are needed, but it does provide useful output semantics (count, quotes array, _source), contributing to overall understanding.
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 ('Fetch') and clearly states the resource: a batch of up to 50 random inspirational quotes from ZenQuotes. It distinguishes itself from siblings like random_quote and today_quote by emphasizing batch size and randomness.
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 gives clear context: it retrieves multiple random quotes, which implies use when a batch is needed. It does not explicitly name alternatives or exclusions, but the purpose is unambiguous enough to guide selection.
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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