calculate_expression

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 key behavioral traits: it uses sympify from sympy, supports symbolic variables (x, y, z), handles special symbols (e.g., pi → 3.1415), and returns a string result or error message. It also lists many example behaviors (e.g., derivatives, integrals). However, it doesn't mention potential limitations like performance, complexity bounds, or specific error conditions beyond 'cannot be parsed or computed.'

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 front-loaded with the core purpose and parameter explanation, but it includes a lengthy list of 14 example expressions. While these examples are informative, they make the description verbose and could be trimmed or summarized. The structure is logical but not optimally concise, as some examples might be redundant for conveying the tool's capabilities.

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 complexity (mathematical computation with sympy), the description is highly complete. It explains the purpose, parameter semantics in detail, behavioral traits, and includes an output schema (returns str or error). With no annotations, it covers all necessary aspects: how to use it, what it does, and what to expect, making it sufficient for an AI agent to invoke correctly.

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 fully compensate. It adds rich semantics: it defines the 'expression' parameter as a 'Mathematical expression' with examples (e.g., '2 + 3*5'), explains special symbol handling (pi → 3.1415), and provides numerous detailed examples showing syntax and usage. This goes far beyond the basic schema, making the parameter's meaning and format clear.

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: 'calculate mathematical expressions using the `sympify` function from `sympy`, parse and compute the input mathematical expression string.' It specifies the exact method (sympify from sympy) and scope (mathematical expressions), making it highly specific. With no sibling tools, differentiation isn't needed, but the description is precise about what it does.

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 extensive examples (e.g., 'Example expressions:') that show various mathematical operations, suggesting when to use it for different types of calculations. However, it lacks explicit guidance on when not to use it or alternatives, and there are no sibling tools to compare against. The examples serve as implicit guidance but aren't structured as explicit rules.

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