Convert raw CI/test logs into structured test results, auto-detecting Jest, Vitest, or Pytest output for easier analysis.
Parse raw CI/test log text and extract structured test results. Auto-detects framework (Jest, Vitest, Pytest) from the log content.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Raw CI log or test output text to parse | |
| framework | No | Force a specific framework instead of auto-detecting |
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 behavioral burden, and it does little beyond stating auto-detection. It does not say what happens when detection fails or the log is malformed, whether parsing is deterministic/pure, or what the structured result looks like. For a tool with zero annotation coverage this is a notable gap.
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 tight sentences with zero waste; the core purpose is front-loaded and the framework detail follows naturally.
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?
There is no output schema, so the description should ideally hint at the shape of the extracted results or at least the failure mode for unparseable logs. It covers inputs adequately but leaves the return contract and error behavior unspecified.
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?
Schema description coverage is 100% and both parameters are documented inline, including the enum values and the override semantics. The description only restates the auto-detect behavior, adding no syntax or format detail beyond the schema, so the baseline 3 applies.
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 (parse) and resource (raw CI/test log text) plus the outcome (extract structured test results), and names the supported frameworks. An agent can tell it apart from the sibling parse_artifact, which implies parsing a stored artifact rather than raw log text.
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 auto-detect default and the force-a-framework escape hatch are stated, which implies when to pass the framework parameter. However, there is no guidance on when to prefer this tool over parse_artifact or how it relates to get_failures/get_test_detail, leaving the selection decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.