directory

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

No annotations provided, so the description must cover behavioral traits. It reveals that answers come from the business's own knowledge base and tools, using live data (not scraped HTML). However, it does not mention authentication, rate limits, or error handling (e.g., if slug is invalid).

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, front-loaded with the action. Every sentence contributes meaningful information: what the tool does, how answers are generated, and where to get the slug. 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?

For a tool with 2 parameters and no output schema, the description explains the source of answers and their live nature. It lacks details on return format or possible errors, but given the simplicity and the presence of a sibling for slug retrieval, it is fairly complete.

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%, so the baseline is 3. The description adds minimal extra meaning beyond the schema: it hints that slug comes from 'search_businesses' and that the question is to be asked. No further enriching details are provided.

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 (ask a question) and the resource (a specific business's ANOTS agent, identified by slug). It distinguishes itself from siblings: 'search_businesses' is for getting slugs, and 'find_and_ask' likely involves searching; this tool is for a specific known business.

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 tells users to get the slug from 'search_businesses', providing a clear prerequisite. While it doesn't explicitly state when not to use this tool versus alternatives, the context of asking a specific business agent is clear and distinct from siblings.

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 fully carries the burden of behavioral disclosure. It explains the tool discovers and asks businesses, returning answers, but lacks details on side effects (e.g., does it send real messages?), authentication needs, or rate limits. The behavior is generally clear but not exhaustive.

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, directly stating the action and the benefit. It is front-loaded with the key functionality and contains no redundant words. Every sentence earns its place.

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 and two simple parameters, the description adequately explains the tool's purpose and outcome (answers side by side). It could be improved by specifying the return format or handling of edge cases (e.g., no results), but it is sufficient for typical use.

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?

Both parameters have descriptions in the schema (100% coverage). The description adds context for how they are used (discover and ask in one step) but does not provide details beyond the schema, such as input format constraints or example values. Baseline 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 specifies the tool discovers relevant ANOTS businesses and asks each one in a single step, returning answers side by side. This clearly distinguishes it from siblings: 'search_businesses' (only find) and 'ask_business' (only ask a single business). The verb-resource combination is precise and the use case is explicit.

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 states it is 'the fastest way to answer a purchase-intent or research question across the directory,' providing a clear context for when to use it. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools provide implicit guidance.

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 must convey behavioral traits. It correctly indicates the tool performs a search and returns a list, but does not disclose potential side effects, authentication needs, or rate limits. It implies read-only behavior but doesn't guarantee safety.

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 long, wastes no words, and front-loads the core purpose. It is concise and scannable.

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 adequately lists return fields. Params are fully covered. It lacks error handling or pagination details, but for a simple search tool this is sufficient.

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 does not add significant extra meaning beyond the schema's parameter descriptions. It repeats the query purpose but does not elaborate on formatting or edge cases.

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 searches a directory for businesses whose AI agents can answer questions, and specifies return fields (slug, name, what they do, agent-card URL). This effectively differentiates from sibling tools like 'ask_business' which would query an individual agent.

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 to use this tool first to discover agents before asking them, providing clear usage context. However, it doesn't explicitly state when not to use it or contrast with alternatives like 'find_and_ask'.

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