create_matrix

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 that the tool creates a matrix and stores it (implied by 'key for the stored matrix'), but lacks details on behavioral traits like error handling (e.g., invalid data), side effects (e.g., state persistence), or performance considerations. It adds some context (e.g., optional naming, sequential generation) but is incomplete for a mutation tool.

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 (purpose, args, example, returns) and uses bullet points for readability. It's appropriately sized—each sentence adds value, such as clarifying data types and providing examples. However, the example section is slightly verbose with two cases, though both are illustrative.

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 (2 parameters, mutation operation) and lack of annotations or output schema, the description is partially complete. It covers the core functionality and parameters well but misses behavioral details (e.g., what happens on failure, storage limits) and doesn't fully explain the return value ('key for the stored matrix') in context of the system. It's adequate but has gaps for safe agent use.

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 effectively explains both parameters: 'matrix_data' as 'a list of lists representing rows and columns' with examples of numeric and string expressions, and 'matrix_var_name' as an optional name with default behavior. This adds significant meaning beyond the bare schema, though it doesn't cover all edge cases (e.g., empty lists).

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 tool's purpose: 'Creates a SymPy matrix from the provided data.' It specifies the verb ('creates') and resource ('SymPy matrix'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'create_vector_field' or 'create_custom_metric', which might also create mathematical objects.

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 doesn't mention sibling tools like 'matrix_determinant' or 'solve_linear_system' that might be related, nor does it specify prerequisites (e.g., needing SymPy installed or defined variables). The examples show usage but don't explain context or alternatives.

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