Execute seeded prompt-regression fixtures locally without API calls, then save the run for reviewing baseline vs. candidate checks, failures, and token usage.
Run seeded fixtures with zero API calls; persist a local run. This is not model performance.
| 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 declare readOnlyHint=false, openWorldHint=false, destructiveHint=false, so the agent knows it writes but is non-destructive and non-networked. The description meaningfully adds that it makes 'zero API calls' and persists a 'local run', explaining the write behavior without contradicting any annotation.
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?
Two terse, front-loaded sentences with no filler. The core action leads and the disambiguation follows.
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?
For a zero-parameter tool with no output schema, the description covers the essential behaviors: no external calls, local persistence, and non-evaluation purpose. It could say more about what a 'run' record contains, but nothing critical is missing.
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, and schema coverage is 100%, so there is nothing for the description to compensate for. Baseline of 4 applies since no parameter semantics are needed.
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 + resource ('Run seeded fixtures') and adds scope qualifiers ('zero API calls', 'persist a local run'). The clarifying line 'This is not model performance' helps an agent rule out an evaluation interpretation, though it doesn't name a sibling tool directly.
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 description says what it is not ('not model performance') but gives no explicit when-to-use guidance or prerequisites for running a demo. Compared to the read-oriented siblings (list_cases, list_runs, get_run), the write nature is inferable only from annotations, not the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.