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. It mentions 'compute' but doesn't disclose behavioral traits such as whether it handles symbolic sets, returns symbolic expressions, or has any side effects. For a mathematical tool with no annotation coverage, this is a significant gap.

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 waste, front-loading the core action and parameters appropriately. It earns its place by succinctly conveying the tool's purpose.

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 low complexity (simple set operation), high schema coverage (100%), and the presence of an output schema, the description is complete enough for basic use. However, it lacks context on mathematical behavior or sibling differentiation, preventing a perfect score.

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 clear parameter descriptions ('Subset' and 'Universal set'), so the schema does the heavy lifting. The description adds no additional meaning beyond what the schema provides, meeting the baseline of 3.

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 ('complement of set1 in set2'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'sympy_intersection' or 'sympy_union', which would require a 5.

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 like 'sympy_intersection' or 'sympy_union', nor any context about prerequisites or mathematical scenarios. The description only states what it does, not when to apply it.

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