get_dec_exampleReturns a complete sample decision record as raw markdown, covering every section, so you can learn the required ADR structure and frontmatter.
Return a complete, valid sample decision 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 supplied, so the description carries the burden alone. It does disclose meaningful traits: the output is raw markdown with both frontmatter and body, it is complete and valid, and it 'exercise[s] every section' — telling the agent the sample is exhaustive rather than minimal. It stops short of saying whether the sample is static or parameterized, but the zero-parameter schema makes that moot.
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, front-loaded with the verb and resource, with the qualifiers ('frontmatter and body', 'every section') placed as trailing detail. Every clause earns its place and nothing is padded.
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 structure need not be restated, and the description adds the useful fact that the content is markdown with frontmatter plus an exhaustive section set. Complete enough to call correctly, though a note on whether the example is a fixed canned file would remove the last ambiguity.
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 no parameters (0 params, 0 required), so there is nothing for the description to disambiguate. Baseline of 4 applies; the description correctly avoids inventing parameter talk.
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 ('Return') and resource ('sample decision document') plus the exact payload shape ('raw markdown -- frontmatter and body'). This cleanly separates it from sibling get_dec (a real document), get_dec_template (a blank skeleton), and create_dec, without needing to name them.
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?
'For use as a learning example' implies the usage context but never states when to prefer this over get_dec, get_dec_template, or get_dec_example's many sibling example tools. No exclusions or prerequisites are given; an agent must infer the routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.