Select a random algorithm problem filtered by topic and difficulty for targeted practice.
Pick a random problem by topic and/or difficulty
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | ||
| difficulty | No |
Changes observed during successful MCP inspections.
v0.3.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool picks a random problem, but doesn't disclose whether the selection is truly random, whether it returns a problem ID or full text, whether it can return no result if no match, or any side effects. For a selection tool, the randomness and return behavior are important but undisclosed.
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, concise sentence that front-loads the action and criteria. Every word earns its place; no filler or redundancy.
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 no annotations, no output schema, and 0% schema description coverage, the description is too thin. It doesn't explain what the return value looks like (e.g., problem statement, ID, link), what happens if no problem matches the filters, or whether the selection is truly random. Given the sibling tools like get_solution and get_theory, an agent would benefit from knowing how the picked problem is returned and how to use it next.
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 0%, but the description mentions 'topic' and 'difficulty' as filters, which maps directly to the two parameters. The enums in the schema already define valid values, so the description adds minimal meaning beyond what the schema provides. It doesn't explain optionality (both are optional since required is 0) or behavior when both are omitted, but the description's phrase 'and/or' implies that.
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 'Pick a random problem by topic and/or difficulty' clearly states the action (pick a random problem) and the filtering criteria (topic and/or difficulty). It distinguishes itself from sibling tools like get_solution or get_theory, which are about retrieving content rather than selecting a problem. However, it doesn't explicitly name a sibling alternative, so it doesn't fully differentiate from a potential 'get_problem' tool.
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 implies usage: use this tool when you need a random problem filtered by topic/difficulty. It doesn't explicitly state when not to use it or name alternatives, but the sibling list (get_solution, get_theory, etc.) makes the context clear. The lack of explicit exclusions or alternative routing leaves some room for inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.