Symbolic Algebra MCP Server

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

With no annotations provided, the description carries the full burden. It discloses the mathematical operation and return format, but doesn't mention computational complexity, error conditions, or limitations (e.g., matrix size restrictions, numerical stability). It provides basic behavioral information but could be more comprehensive.

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 well-structured with clear sections (Args, Example, Returns) and front-loaded purpose statement. The example is helpful but slightly verbose; every sentence earns its place though some could be more concise.

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 no annotations and no output schema, the description does a good job explaining the tool's purpose, parameter, usage example, and return format. It could benefit from more behavioral details (like computational characteristics), but provides sufficient context for basic usage.

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 schema description coverage is 0%, so the description must compensate. It clearly explains that matrix_key refers to 'The key of the matrix to calculate eigenvectors for' and provides an example showing how to obtain this key. This adds meaningful context beyond the bare schema.

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 specific action ('calculates the eigenvectors of a matrix') and the implementation method ('using SymPy's eigenvects method'), which distinguishes it from sibling tools like matrix_eigenvalues or matrix_determinant. It provides a complete verb+resource+method specification.

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 implies usage through the example showing it requires a matrix created via create_matrix, but doesn't explicitly state when to use this tool versus alternatives like matrix_eigenvalues. It provides some context but lacks explicit guidance on tool selection.

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