Validate test content as JSON string or object before saving. Returns a summary on success or an error if invalid, ensuring only valid tests are created or updated.
Validates test content (JSON string or object) without saving it. Returns {ok: true, doc: summary} or {ok: false, error}. Useful before test_create/test_update.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Suggested name (optional) | |
| content | Yes | Test content: JSON string or object |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool does not save content and specifies the exact return structure ({ok: true, doc: summary} or {ok: false, error}), which are critical behavioral traits. It does not mention any side effects beyond no-save, but that is the primary concern. The description could add more context about validation rules, but it covers the essential behavior.
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?
The description is two sentences with no filler. The main purpose and no-save behavior are front-loaded, followed by the return format and usage guidance. Every sentence earns its place.
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 tool with 2 parameters, no output schema, and no annotations, the description is quite complete. It covers the purpose, the no-save behavior, the return format, and usage guidance. It does not explain what 'summary' contains or any validation specifics, but these are not critical for an agent to invoke it correctly. The description is sufficient for safe and correct use.
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 schema description coverage is 100%, so the schema already documents both parameters (name and content). The description repeats that content is a JSON string or object, which adds little beyond the schema. It does not provide additional context about how the 'name' parameter is used in validation, so the description adds marginal value here. Baseline of 3 is appropriate.
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 clearly states the tool's action ('Validates test content'), the resource (test content), and a key distinguishing trait ('without saving it'). It also specifies the return format, which helps an agent understand what to expect. This is specific and not a tautology.
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 explicitly says 'Useful before test_create/test_update', which gives direct guidance on when to use this tool relative to its siblings. This is an explicit usage cue that eliminates ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.