Lists six fictional labeled cases and evaluation limits for local prompt-regression testing, so you can review coverage before baseline vs candidate runs.
List the six fictional, labeled cases and evaluation limitations.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds genuinely new context: the result set is fixed at six fictional, labeled cases and also surfaces evaluation limitations, which tells the agent what it will be looking at.
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 front-loaded sentence with no filler. The key noun (cases) and the scope constraints (six, fictional, labeled) appear immediately.
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 no parameters, no output schema, and rich annotations, the description only needs to characterize the returned content, which it does by naming the six fictional cases and evaluation limitations. Slightly more on what a 'case' or a 'limitation' entry contains would make it fully self-sufficient.
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 the baseline of 4 applies. There is nothing for the description to disambiguate, and it correctly implies the call is argument-free.
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 (List) and resource (cases) and even quantifies the scope: six fictional, labeled cases plus evaluation limitations. It is clearly distinct from run_demo/list_runs/get_run, but it never explicitly contrasts itself with those siblings, so it lands just short of a 5.
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?
There is no statement of when to call this versus run_demo or list_runs, no prerequisites, and no indication that this is the discovery step before running a case. Any usage intent must be inferred entirely by the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.