mcp-sympy

Does 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. 'Numerical simplification' gives minimal insight: it suggests a read-only operation but doesn't confirm safety, describe output format, mention performance or limitations, or explain what 'simplification' entails (e.g., rounding, approximation). This is inadequate for a tool with potential computational complexity.

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 extremely concise with just two words, front-loaded and without any wasted language. Every word ('Numerical', 'simplification') contributes to the core meaning, making it efficient despite its shortcomings in other dimensions. It avoids redundancy and is structured for quick scanning.

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's complexity (mathematical simplification), lack of annotations, low schema coverage, and many siblings, the description is incomplete. It doesn't explain behavior, parameters, or usage context. While an output schema exists (which might cover return values), the description fails to provide enough context for the agent to understand when and how to invoke this tool effectively amidst alternatives.

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 1 parameter with 0% description coverage, so the schema provides no semantic information. The description adds no parameter details—it doesn't explain what 'expr' represents (e.g., a mathematical expression as a string), its expected format, or examples. This fails to compensate for the schema's lack of documentation, leaving the parameter meaning unclear.

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 'Numerical simplification' states a general purpose but is vague. It specifies the action ('simplification') and hints at the domain ('numerical'), but doesn't clarify what resource it operates on (e.g., mathematical expressions) or how it differs from sibling tools like 'sympy_simplify' or 'sympy_ratsimp'. It avoids tautology but lacks specificity for sibling differentiation.

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. With many sibling tools (e.g., 'sympy_simplify', 'sympy_ratsimp', 'sympy_nsolve'), there is no indication of context, prerequisites, or exclusions. Usage is implied only by the name and vague description, leaving the agent to guess based on tool naming patterns.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.