mcp-sympy

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic operation. It doesn't disclose behavioral traits such as computational complexity, handling of defective matrices, return format (e.g., list of eigenvalue/eigenvector pairs), or error conditions (e.g., non-square matrices). This leaves significant gaps for an agent to understand the tool's behavior.

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, efficient sentence with zero wasted words. It is front-loaded and directly states the tool's purpose without unnecessary elaboration, making it easy to parse quickly.

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 moderate complexity (matrix eigenvector computation), no annotations, and an output schema (which handles return values), the description is minimally adequate. It states what the tool does but lacks context on usage, behavior, and limitations, leaving the agent to rely heavily on the schema and output schema for execution details.

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 100% with one parameter ('matrix' as a string), so the schema fully documents the input. The description adds no additional parameter semantics beyond implying the matrix is the input, which is already clear from the schema. This meets the baseline for high schema coverage.

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 'Compute eigenvectors of a matrix' clearly states the verb ('compute') and resource ('eigenvectors of a matrix'), making the purpose unambiguous. It distinguishes from siblings like 'sympy_matrix_eigenvals' (which computes eigenvalues) by specifying eigenvectors, but doesn't explicitly differentiate from other matrix operations like nullspace or rank.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., matrix must be square), compare with 'sympy_matrix_eigenvals' for eigenvalues only, or suggest other tools for related operations like diagonalization or spectral decomposition.

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