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A calculator that doubles all results - because why not? Interprets natural language math questions and returns twice the correct answer.
This server has significant definition quality gaps. While the tools are explicitly registered with schemas, the descriptions lack depth and specificity for LLM decision-making. The `lazy_calculate` tool has a deceptive description that advertises intentional incorrectness, which violates core agent tool patterns, agents expect tools to return correct results. The `explain_laziness` tool has a minimal description (58 chars) and empty input schema with no properties documented. Neither tool has parameter descriptions, and output schemas are completely absent from the tool definitions. The naming follows basic conventions (verb_noun style), but the deliberate malfunction makes this unsuitable for production agent use.
Explains why this calculator is lazy and doubles all results
Performs lazy calculation on a math question - returns double the actual answer. This calculator interprets natural language math questions and always returns twice the correct answer. Because sometimes being wrong is twice as fun! Examples: - "What is 2 + 2?" returns 8 (actual: 4) - "Calculate 10 divided by 2" returns 10 (actual: 5) - "What's 3 times 4?" returns 24 (actual: 12)
Tool `lazy_calculate` intentionally returns incorrect results (double the correct answer). Agents depend on tools returning truthful, accurate data. A tool that deliberately doubles answers violates the foundational trust model and will cause agents to make wrong decisions and produce incorrect outputs to users.
Missing output schema documentation. Neither tool documents what fields are returned or their types. LLMs cannot plan downstream actions or extract chained references (e.g., IDs for follow-up calls) without knowing the response structure.
Input parameters lack descriptions. The `lazy_calculate` tool accepts a 'question' parameter with a type string, but the parameter itself has no description explaining what format of questions are expected, what natural language patterns are supported, or examples of valid input.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 40 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 20 | - | v1 |
Tool description for `explain_laziness` is only 58 characters and lacks actionable context. It does not explain when or why an LLM would invoke this tool, what information it provides, or how it contributes to solving user problems.
No error handling or recovery guidance. The code includes basic validation (checking if expression is empty) but returns generic messages. Agents receive no guidance on how to refine their input or what constraints apply to the question format.
The `explain_laziness` tool has an empty inputSchema with no properties. This is valid JSON Schema, but the tool serves no clear purpose in an agent context, it does not accept parameters and appears to be documentation rather than a functional tool.