Get LLM API summary
get_llms_txtRead-only
Return a compact LLM-readable summary of the Quiz.Video API.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
api
by video.quiz
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already communicates that this is a safe read operation. The description adds that the output is 'compact' and 'LLM-readable,' but does not disclose details about the summary's format, length, or content scope, leaving some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that delivers the core purpose without unnecessary detail or repetition. It is appropriately concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only summary tool, the description conveys the essential function. However, it could briefly note that this is a high-level overview versus structured API specifications, which would help the agent choose appropriately among sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is fully complete. The description correctly avoids parameter information, and with no parameters, no additional semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action: 'Return a compact LLM-readable summary of the Quiz.Video API.' It identifies the resource (Quiz.Video API) and the output format (compact LLM-readable summary), distinguishing it from sibling tools like get_api_catalog and get_openapi_spec.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when an LLM needs a compact API summary, but it does not explicitly differentiate when to use this tool over get_api_catalog or get_openapi_spec. No exclusions or alternative references are provided.
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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TDQS
A3.7/5.0
Disambiguation5/5
Every tool targets a distinct resource and action (create/get/list/update/delete per entity). Overlapping pairs like get_render vs download_render are clearly differentiated by their purpose, and generate_quiz vs create_quiz serve different creation modes. There is no ambiguity between tools.
Naming Consistency4/5
The vast majority of tools follow a consistent 'quiz_video_<verb>_<noun>' pattern in snake_case. The only deviation is the three discovery tools (get_api_catalog, get_llms_txt, get_openapi_spec) which omit the 'quiz_video_' prefix, though they still use a clear verb_noun structure.
Tool Count3/5
With 30 tools, the server is on the heavier side and exceeds the typical 15-tool guideline. However, the count is justified by the breadth of resources covered (quizzes, questions, hooks, templates, flashcard decks, renders, music, account) and each tool has a distinct role, so the size feels appropriate for a comprehensive API surface.
Completeness4/5
The server covers full CRUD for most core resources (quizzes, templates, flashcard decks, hooks) and add/list for questions. Minor gaps include no individual question update/delete and no direct render deletion, but these are not critical and the main workflows (create → view → render → download) are fully covered.