math_ls

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

With no annotations, the description carries the behavioral burden. It discloses two call modes and what each returns: categories plus flat list of name/intent without category, and full descriptors with name/description/inputSchema with category. Minor edge behaviors like invalid category handling are not mentioned, but core behavior is clear.

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?

Three dense sentences, front-loaded with the tool's core purpose. The workflow phrasing is efficient and every sentence contributes useful routing information without 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 one-optional-parameter listing tool with an output schema, the description fully covers both invocation modes and the surrounding discovery workflow. An agent has everything it needs to select and call this tool 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 already provides 100% coverage for the optional category parameter, including what happens when omitted. The description adds meaningful extra semantics by specifying that category changes output granularity to full descriptors per tool, going beyond the schema's basic description.

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?

Explicitly states it lists available math tools, and the description clearly separates this from math_man (one tool's parameters) and math (running a tool). The purpose is immediately actionable and distinguishable from siblings.

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?

Provides a clear workflow: call with no args for categories and a flat list, use math_man(name) for one tool's parameters, use math_ls(category) for full descriptors, and finally call math(name, arguments) to run. This gives explicit when-to-use guidance and names the alternatives.

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