get_dog_problem_articleReturn the TL;DR, section overview, and CTA URL for a single article from the DLA library, by its slug. Use after search_dog_problems finds a match. Does NOT return the exact protocol (that lives inside the paid programs) — only the diagnostic content and the CTA to the free assessment.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The article slug (from search results). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It details what the tool returns (TL;DR, section overview, CTA URL) and what it does not return (protocol), giving agents a clear expectation of the response scope.
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, immediately front-loading the returned content and usage context. Every sentence is essential with no redundant information.
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 simplicity (single parameter, no output schema), the description fully covers inputs, outputs, and limitations. It explains the return type and what is excluded, making it complete for an agent to select and 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 input schema already describes the 'slug' parameter, achieving 100% schema coverage. The description adds value by specifying 'from search results,' clarifying the source of the slug beyond the schema's generic 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?
The description clearly states the tool returns 'TL;DR, section overview, and CTA URL for a single article from the DLA library, by its slug.' It distinguishes from siblings like search_dog_problems and get_program_info by specifying it is for diagnostic content and not the protocol.
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 advises 'Use after `search_dog_problems` finds a match,' providing clear context for when to invoke this tool. It does not, however, explicitly list when not to use it or mention alternatives like get_program_info, but the context is sufficient.
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.