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. It states the action ('compute eigenvalues') but lacks details on computational complexity, error handling (e.g., for non-square matrices), output format, or any side effects. This is insufficient for a mutation tool (eigenvalue computation implies processing).

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, direct sentence with zero wasted words. It is front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it highly concise and well-structured.

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 (matrix eigenvalue computation), the description is minimal. While an output schema exists (relieving the need to explain return values), the lack of annotations and behavioral details leaves gaps. The description is adequate but incomplete for a mathematical operation with potential nuances.

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 100% description coverage, with the 'matrix' parameter documented as 'Matrix string'. The description does not add any additional semantic context beyond this, such as format examples (e.g., '[[1,2],[3,4]]') or constraints. Baseline 3 is appropriate given the schema's completeness.

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 clearly states the verb ('compute') and resource ('eigenvalues of a matrix'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'sympy_matrix_eigenvects' (which computes eigenvectors), leaving room for improvement in sibling distinction.

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. It does not mention sibling tools (e.g., 'sympy_matrix_eigenvects' for eigenvectors) or specify scenarios where eigenvalues are needed over other matrix operations, offering minimal usage context.

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