get_uc_exampleReturn a complete sample use case document as raw markdown, including frontmatter and body, to learn the required structure and sections.
Return a complete, valid sample use case 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?
With no annotations, the description carries the full behavioral burden, and it does disclose meaningful traits: the return is raw markdown, includes both frontmatter and body, and is guaranteed complete and valid by exercising every section. What it does not say -- whether the sample is static or generated, and its approximate size -- is minor for a zero-parameter read-only tool.
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 no filler, front-loading the action and output type before the purpose clause. Every phrase ('complete, valid', 'frontmatter and body', 'every section', 'learning example') carries distinct information.
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?
An output schema exists, so return shape need not be re-explained, and the description correctly focuses on what the sample contains and why. The only gap is the absence of any statement about how this differs from the template and get siblings in this large family.
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 clarify beyond the schema. Baseline 4 applies; no parameter semantics are missing.
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?
States a specific verb and resource ('Return a complete, valid sample use case document as raw markdown') and pins down the output form ('frontmatter and body -- exercising every section'). It implicitly separates itself from siblings like get_uc_template via 'for use as a learning example', but never names an alternative explicitly, so the differentiation is inferential rather than stated.
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' hints at the intended context, so usage is implied rather than stated. There is no explicit when-to-use versus get_uc_template, get_uc_example-style counterparts for other artifact types, or get_uc, and no exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.