mcp-sympy

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

No annotations are provided, so the description carries full burden. 'Arc cosine' implies a mathematical computation but doesn't disclose behavioral traits like error handling for invalid inputs, symbolic vs numeric evaluation, or output format. It mentions no constraints beyond the schema's range hint.

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?

Extremely concise with 'Arc cosine.' as a single phrase, front-loaded and zero waste. It efficiently conveys the core function without unnecessary elaboration.

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 (single parameter, 100% schema coverage, output schema exists), the description is minimally adequate. However, without annotations and with only basic purpose stated, it lacks depth for effective agent use, such as explaining symbolic nature or error conditions.

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 the parameter 'expr' documented as 'Expression (between -1 and 1)'. The description adds no additional meaning beyond this, so it meets the baseline of 3 where the schema does the heavy lifting.

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 'Arc cosine' states the mathematical function but is vague about its implementation as a tool. It distinguishes from siblings like 'sympy_cos' by indicating the inverse operation, but doesn't specify it's for symbolic computation or that it returns a symbolic expression.

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 on when to use this tool versus alternatives like 'sympy_acos' vs 'sympy_asin' or 'sympy_atan'. The description lacks context about typical use cases, prerequisites, or comparisons with sibling tools.

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