List Luogu Algorithm Topics

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

The annotations (readOnlyHint=true, openWorldHint=false) already indicate the tool is read-only and not open-world. The description adds value by specifying the content of the output (canonical topics, aliases, tag ids), providing behavioral context beyond the annotations.

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 sentence that is concise and front-loaded with the main action ('List canonical algorithm topics'). Every part of the sentence is informative, with no wasted words.

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 no parameters, no output schema, and the presence of sibling tools, the description is largely complete. It explains what the tool returns and its role. However, it could mention that the list is static or cached, but this is a minor gap.

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?

There are no parameters, so schema coverage is 100%. The description does not need to explain parameters, but it adds meaning by describing the output structure. For a parameterless tool, this is appropriate and provides useful context.

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 action ('List'), the resource ('canonical algorithm topics'), and specifies the output components ('aliases, and known Luogu tag ids'). It also distinguishes itself from sibling tools like luogu_find_topic_problems which find problems using topics, while this tool lists the topics themselves.

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 this tool is used to obtain the list of algorithm topics for subsequent searches ('used by the high-level topic search route'). However, it does not explicitly state when to use this tool over alternatives or provide exclusion criteria. Usage context is implied but not fully specified.

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