Execute all tests with detailed output to identify issues in log analysis workflows, providing comprehensive debugging information for the Log Analyzer MCP server.
Run all tests with verbose output (verbosity level 1).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.8Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the verbosity level but fails to describe what 'verbose output' entails, whether it affects performance, if it's read-only or destructive, or any other behavioral traits like error handling or output format. This leaves significant gaps in understanding the tool's 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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is front-loaded and wastes no space, making it easy to parse and understand quickly.
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?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description's job is simplified. It covers the basic action and verbosity but lacks details on behavioral aspects like what 'verbose' means or how it differs from siblings. This makes it minimally viable but with clear gaps in context, especially with no annotations to supplement.
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 input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately adds no parameter details, focusing solely on the tool's action and verbosity level, which aligns with the schema's completeness. A baseline of 4 is applied as it compensates adequately for the lack of parameters.
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 action ('Run all tests') and specifies the verbosity level ('verbose output (verbosity level 1)'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'run_tests_no_verbosity' or 'run_tests_very_verbose' beyond the verbosity level, which prevents a perfect score.
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 provides no guidance on when to use this tool versus alternatives, such as 'run_tests_no_verbosity' or 'run_tests_very_verbose', nor does it mention any prerequisites or context for usage. This lack of comparative or contextual information limits its helpfulness for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.