get_qa_exampleReturns a complete sample QA document in raw markdown, including frontmatter and body, to demonstrate every section as a learning example.
Return a complete, valid sample QA document as raw markdown -- frontmatter and body -- exercising every section, for use as a learning example.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.36.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden, and it does disclose meaningful traits: the return is raw markdown, includes both frontmatter and body, and is guaranteed complete and valid with every section exercised. That is well beyond a bare restatement, though it says nothing about read-only safety or size/cost of the payload.
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?
A single sentence with the action and the return shape front-loaded, then the qualifiers (frontmatter, body, every section) and the intended use. Every clause earns its place with no padding.
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 an output schema present, the description need not explain return values, and it already characterizes the payload's format and completeness. For a zero-parameter example tool this is nearly complete; the only omission is an explicit tie-breaker against get_qa_template and the other get_*_example siblings.
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 takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a no-parameter tool is 4. The description adds no parameter detail, but none is required.
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 states a specific verb and resource ('Return a complete, valid sample QA document') and specifies the exact output shape (raw markdown, frontmatter and body, every section exercised). It is clearly distinct in spirit from the many sibling example tools, but it never names the closest sibling alternative, get_qa_template, which a reader must infer is the fill-in-the-blank counterpart.
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 phrase 'for use as a learning example' implies a usage context, but there is no explicit when-to-use, when-not-to-use, or alternative routing. An agent choosing between get_qa, get_qa_template, and get_qa_example gets only an implied signal rather than a stated rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.