Return aggregated performance statistics for a Leetcode problem using its ID, saving LLMs from manually calculating totals each time.
Return the aggregated performance statistics for a Leetcode Problem This becomes useful because LLM doesn't need to recieve and manually calculate everything every time
| Name | Required | Description | Default |
|---|---|---|---|
| leetcode_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It hints that results are pre-aggregated (saving the agent computation), which is a genuine behavioral trait, but says nothing about scope (all attempts vs recent), permissions, cost, or freshness of the aggregation.
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 first sentence is front-loaded and efficient. The second sentence is a soft justification rather than actionable content, and its value is marginal, making the description mildly padded.
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?
An output schema exists, so return values need not be explained. For a one-parameter read tool this is close to adequate, but the aggregation scope (which attempts, what time window) remains ambiguous, which is the main thing an agent needs to call it correctly.
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 single parameter leetcode_id has 0% schema description coverage, so the description must compensate and largely does not. Only the phrase 'for a Leetcode Problem' weakly implies that the id identifies a problem; no format, validity, or lookup behavior is described.
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 and resource: 'Return the aggregated performance statistics for a Leetcode Problem'. This is clearly distinguishable from raw-log siblings like get_attempt_history or log_attempt. It does not, however, name any sibling explicitly, so the differentiation is left to inference.
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?
There is no when-to-use guidance: nothing says when aggregated stats are preferable to get_attempt_history or get_weak_areas. The second sentence offers a rationale ('LLM doesn't need to recieve and manually calculate everything') rather than a selection condition, and contains no exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.