symbolic_calculus

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 doesn't state whether operations require specific input formats, how errors are surfaced, what the return value looks like, or any constraints (e.g., 'integrate' needs a variable, 'limit' needs a point). For a compute tool with zero 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?

One tight sentence, front-loaded with the verb and followed by the operation enumeration. No filler.

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?

For a five-parameter, zero-annotation, no-output-schema compute tool, the description is too thin: it omits parameter semantics, return behavior, and usage context. It tells an agent what operations exist but not how to invoke them 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?

Schema description coverage is 0%, so the description must compensate for five undocumented parameters. It only mentions 'expression' implicitly and lists operation names, without mapping any parameter to its meaning, default, or usage (e.g., 'order' applies to series/diff, 'point' to limit). No semantic guidance is added.

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?

States a specific verb (perform symbolic calculus) and resource (an expression), then enumerates the operations (diff, integrate, limit, series, simplify, expand, factor). This distinguishes it from siblings like symbolic_solve and symbolic_evaluate, though it doesn't explicitly name them.

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 when-to-use guidance, no alternatives named, no exclusions. The agent must infer from the operation list how this differs from symbolic_solve or symbolic_evaluate.

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