Generate comprehensive test cases, including edge cases, for any algorithm problem by providing its slug. Validate your solution against diverse inputs.
Generate test cases (including edge cases) for a problem
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Problem slug |
Changes observed during successful MCP inspections.
v0.3.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden, but it only promises generation and edge-case inclusion. It doesn't disclose return format, whether code is executed, side effects, or limitations. This leaves significant behavior unexplained.
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?
Single sentence with no filler and the key qualifier 'including edge cases' front-loaded. It is compact, though it omits details that other dimensions penalize.
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 no output schema and no annotations, the agent is left without information about what the tool returns or how results are presented. The one-parameter input is clear, but the response/usage context is under-specified. More detail is needed for a complete picture.
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 coverage is 100%, with 'Problem slug' already documented. The description repeats 'problem' without adding syntax, constraints, or additional meaning. Baseline 3 is appropriate since the schema does the heavy lifting.
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 states a clear action (generate) and object (test cases, including edge cases) tied to a problem. This distinguishes it from sibling tools like get_solution or get_theory, though it doesn't elaborate on test-case format or scope. It is clear but not maximally specific.
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?
No guidance on when to prefer this tool over siblings such as get_real_world_cases or run_user_code. There is no mention of prerequisites, workflow position, or exclusions. The only hint is the slug parameter, which implicitly requires a selected problem.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.