Dog Leadership Academy

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

With no annotations, the description carries the full burden. It discloses that the tool returns a URL for browser payment and that the agent should never handle card details. However, it does not mention potential side effects, authentication needs, or URL expiration.

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 sentences with no wasted words. The critical information (what it does, return type, agent guidelines) is front-loaded and precise.

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 output schema, the description explains the return value (URL) and the flow (user opens in browser). It does not mention error conditions or prerequisites beyond the explicit usage guideline, but it is sufficient for a straightforward tool.

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 coverage is 100%, so the description adds minimal value beyond what the schema already documents. It contextualizes the params (for enrollment), but does not elaborate on their syntax or constraints beyond the enum.

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 creates a Stripe checkout URL for enrollment in a chosen tier. It distinguishes itself from sibling tools, which deal with dog problems and program info, by focusing on payment enrollment.

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?

Explicitly states when to call: 'Only call after the user has explicitly chosen a tier and asked to enroll.' Also instructs the agent not to handle payment details, providing clear usage boundaries.

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

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

With no annotations, the description carries the full burden. It discloses the tool returns only diagnostic content and a CTA, not the full protocol. This is sufficient for a simple read operation, though it could mention any additional constraints like authentication or idempotency.

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?

Two sentences, no fluff. The first sentence states purpose and input, the second adds usage context and exclusions. Highly efficient and front-loaded.

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 simple input schema (1 parameter, no enums, no output schema) and no annotations, the description covers the main points: what is returned, what is not, and when to use. Minor omission is lack of output format details, but acceptable without an output schema.

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 100% and the schema already describes the slug parameter. The description adds only a usage hint ('from search results'), which is a minor addition. Baseline score of 3 is appropriate.

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 TL;DR, section overview, and CTA URL for a single article by slug. It distinguishes from sibling tools by mentioning it is used after search_dog_problems and does not return the exact 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 says 'Use after search_dog_problems finds a match', providing clear when-to-use guidance. It also clarifies what is not returned (the exact protocol), helping the agent avoid misuse. However, it does not explicitly list alternatives for other scenarios.

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

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

No annotations provided, so description carries full burden. Describes return content: pricing, included items, sales pitch. Explains tier behavior. No side effects mentioned, but appropriate for a read-only tool.

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?

Two sentences, purpose front-loaded, no redundancy. Every sentence adds value.

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?

Covers what the tool returns (pricing, inclusions, pitch) and parameter usage. Lacks data freshness details, but sufficient for a simple info retrieval tool with no output schema.

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 coverage is 100% with description for tier parameter. Description adds reinforcement of options and omission behavior, but does not provide new meaning beyond schema.

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?

Clearly states the tool returns DLA program info, pricing, included items, and sales pitch. Distinct from siblings which handle enrollments, problems, and assessments.

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?

Explicitly says 'Use when the user asks how much it costs, what they get, or how DLA works.' Provides parameter options and omission behavior. Lacks explicit when-not-to-use, but sufficient.

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

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

No annotations are provided, so the description carries full burden. It accurately describes a read-only operation returning the taxonomy, with no side effects or contradictions. Minor omission: no mention of result format.

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?

Two sentences: first specifies the output with clusters listed, second gives usage intent. No redundant information, perfectly concise.

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 zero parameters, no output schema, and simple behavior, the description fully covers what the tool does and when to use it. Sibling tools are distinct, so completeness is high.

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 (0 params), so baseline is 4. The description adds no parameter information, which is appropriate since none exist.

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, listing the six clusters and article titles. It distinguishes itself from siblings like get_dog_problem_article and search_dog_problems.

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 includes 'Use to enumerate what the library covers,' providing a clear usage hint. It implicitly guides when to use it but lacks explicit when-not-to-use or alternative suggestions.

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

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

With no annotations, the description carries the full burden. It discloses the return fields (title, one-line description, TL;DR summary, article URL, CTA URL) and implies search behavior via examples. It does not detail sorting, empty results, or search scope, but the information provided is sufficient for an agent to understand the tool's behavior.

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, focused paragraph with no extraneous information. It is front-loaded with purpose, followed by return details and usage examples. Every sentence adds value.

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 two well-documented parameters, no output schema, and no annotations, the description provides sufficient context: returns, examples, and scope. It could mention relations to siblings like get_dog_problem_article, but the examples implicitly guide use. Overall adequate for the tool's complexity.

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 coverage is 100% (both parameters described). The description adds value by providing example queries, clarifying that 'query' expects natural language from the owner. This reinforces the schema without repeating it verbatim, earning above baseline.

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 it searches a library of ~70 common dog behaviour problems and returns specific fields (title, description, summary, URLs). Example queries like 'my dog pulls on the leash' differentiate it from sibling tools like get_dog_problem_article or list_dog_problem_topics, which have different purposes.

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 for questions like...' providing good context for when to use the tool. However, it does not mention when not to use it or suggest alternative sibling tools for specific cases (e.g., retrieving a single article by ID).

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

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

With no annotations provided, the description takes on full burden. It reveals key behavioral traits: submission goes directly to the intake system, triggers a personalized analysis sent to the owner's email within minutes, and is authored by specific persons. It omits potential side effects like duplicate handling or error states, but still provides substantial transparency.

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 focused paragraph that efficiently covers purpose, prerequisites, and outcome. It is concise without omitting critical information. Minor improvement could be structuring into bullet points or separating prerequisites, but current form is clear.

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 10 parameters and no output schema, the description adequately explains the tool's inputs (email, dog name, problem) and outputs (analysis sent to email). It also notes the tool is free and the assessment is written by specific experts. It lacks details on error handling or validation, but sufficiently covers the main context.

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 coverage is 100%, so baseline is 3. The description reiterates required parameters (owner_email, dog_name, primary_problem) but adds no additional semantic meaning beyond the schema's descriptions. The reference to '8 questions' implies the parameters correspond to those, but no further details are given.

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 submits an 8-question free behavioural assessment on behalf of the user. It distinguishes this from siblings by specifying the action and outcome: sending a personalized analysis to the owner's email. No other sibling tool performs this function.

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 states when to call: only when the user has asked for the assessment and provided email, dog name, and answers. It does not explicitly mention alternatives, but given sibling tools, none are substitutes for submission, making this guidance sufficiently clear.

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