list_dog_problem_topicsReturn the taxonomy of the DLA library: six clusters (Reactivity, Loose-leash walking, Puppy foundations, Obedience/recall, Household behaviour problems, Method & philosophy) and the article titles in each. Use to enumerate what the library covers.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It accurately describes the behavior (returning taxonomy) with no side effects. Could add that it is a read-only, non-destructive operation, but the description is already clear enough for an agent.
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 two sentences with no wasted words. It front-loads the action and provides the key details (six clusters) efficiently.
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 simplicity (no parameters, no output schema), the description is largely complete. It names the clusters and indicates the structure. A minor gap is that it doesn't explicitly state the output format (e.g., list of objects), but it's inferred well enough.
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 input schema has zero parameters, and schema coverage is 100%. According to the rubric, 0 parameters warrants a baseline of 4. The description adds no param info because none is needed.
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 returns the taxonomy of the DLA library with six named clusters and article titles. It distinguishes itself from siblings like search_dog_problems (for searching) and get_dog_problem_article (for retrieving a single article).
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 explicitly says 'Use to enumerate what the library covers,' providing clear guidance on when to use it. While it doesn't mention when not to use, the context with sibling tools makes it sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clear, distinct purpose. There is no overlap: payment enrollment, article retrieval, program info, topic listing, article search, and assessment submission are all unique operations.
All tool names follow a consistent verb_noun pattern (e.g., create_enrollment_checkout, search_dog_problems). No mixing of conventions or ambiguous verbs.
With 6 tools, the server covers the essential user workflow without being sparse or bloated. Each tool serves a necessary function for the domain.
The tool set covers the full user journey: exploring the library (search, list topics, get article), learning about programs, submitting a free assessment, and enrolling. No obvious gaps for the intended purpose.