knowledge-base

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

With no annotations, the description fully discloses that authentication is none, the tool works without credentials, and clarifies that neither level is an endorsement of quality. It describes the return fields and the constraints of each level.

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 well front-loaded with the main purpose, but the explanation of levels is somewhat lengthy. It could be more concise while retaining all necessary 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 no output schema, the description adequately explains what is returned and the semantics. It does not cover edge cases like errors or rate limits, but for a single-param tool it is mostly 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 coverage is 100%, and the schema already provides a description for the single parameter 'slug'. The tool description does not add additional meaning beyond what is in the 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?

The description clearly states the tool checks what a merchant has been verified to and by whom, distinguishes it from a cached profile, and explains the meaning of different levels. It is very specific and actionable.

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 advises calling this before acting on a claim that matters, and notes that the profile is a cached summary. It also provides staleness guidance (5 minutes). It does not explicitly mention alternatives but context is clear.

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. Discloses read-only nature, polling requirement, authentication mechanism, and return value. All behavioral traits are transparent.

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, each adding value: purpose, return value, usage hint. No wasted words. Front-loaded with the action.

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, description adequately covers return type (whole thread and status). Also explains authentication, which is essential for using the tool. Complete for a two-parameter 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%, but description adds critical context: parameters are credentials from contact_business. This enhances understanding beyond the schema's basic 'returned by contact_business'.

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 verb 'Read' and resource 'conversation you opened with contact_business'. Distinguishes from sibling contact_business by focusing on reading replies rather than initiating. The purpose is explicit and unambiguous.

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?

Provides clear usage context: polling required ('Poll it; there is no push') and authentication credentials from contact_business. Does not explicitly state when not to use, but implicitly suggests using after contact_business and before send_followup.

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 carries full responsibility. It discloses that the tool is asynchronous, non-obligatory, returns a secret unrecoverable token, requires bearer authentication, has daily and burst quotas, and an alarm for contacting many businesses. This is comprehensive behavioral information beyond what any structured annotation would provide.

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 front-loaded with the primary action, followed by return values, token importance, and authentication details. It is structured logically and every sentence adds value. Could be trimmed slightly, but generally concise for the amount of information conveyed.

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?

The description covers the tool's purpose, return values, critical token behavior, authentication, rate limits, and related tools. It lacks explicit error handling beyond refusal naming the limit, but given the absence of output schema and annotations, it provides sufficient context for an AI agent to use the tool effectively.

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 100% description coverage for all 6 parameters, so the schema already documents each parameter's meaning. The tool description does not add new parameter-level semantics beyond the schema; it only provides context about the slug coming from search_businesses and the importance of the token. 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 clearly states the action ('Open a conversation with the person behind a listing'), specifies the asynchronous nature, and distinguishes from sibling tools by explaining that check_replies and send_followup are for reading replies and writing again. It also references search_businesses implicitly through the slug field.

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 provides guidelines by explaining the asynchronous model, emphasizing that the token is critical and must be saved, and detailing authentication requirements and rate limits. It contrasts with 'reading the knowledge base' to indicate when authentication is not needed, though it does not explicitly state when to use or not use this tool versus alternatives.

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 accounts for behavioral transparency. It discloses that absent information is named in a 'missing' field (not dropped silently), unknown slugs return candidate slugs, and no authentication is needed. This covers important edge cases and safety considerations.

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 concise and well-structured, front-loading the core purpose and then adding relevant details about missing fields, error handling, and authentication. Every sentence serves a purpose without redundancy.

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 is highly complete: it lists the categories of returned data, explains the 'missing' field convention, describes behavior for invalid slugs, and confirms no credentials are needed. This enables an agent to confidently invoke the 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%, but the description adds value beyond the schema by explaining the default fallback logic for the 'kind' parameter (entity then website) and specifying that 'slug' comes from a search_businesses result. This provides essential context beyond parameter names and enum values.

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 reads the full business profile, listing specific fields like identity, contact, address, social profiles, offerings, FAQ, and verification. It distinguishes from siblings like search_businesses (searching) and contact_business (contacting) by focusing on reading a complete record.

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 notes the tool requires no credentials and explains behavior for unknown slugs, but does not explicitly state when to use this tool over alternatives like search_businesses or contact_business. However, the context is clear enough for an agent to infer appropriate use.

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 fully discloses behavior: authentication is none, it works without credentials, returns an array with specific fields, explains the verification object and its levels, and describes the empty result response. No contradictions.

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 paragraph of 6 sentences, front-loaded with purpose and key advice. Every sentence adds unique information (purpose, entry-point, return format, verification, error handling, authentication). 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 3 parameters, no output schema, and no annotations, the description covers all critical aspects: purpose, usage context, return structure with verification, behavior on empty results, authentication, and parameter tips. It is fully complete for agent decision-making.

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 baseline is 3. The description adds significant value beyond the schema: explains query matching logic (words independently, accents ignored, examples), default limit, and optional type. This enhances usability.

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 explicitly states the tool's action ('find'), resource ('businesses, merchants and websites'), and scope ('by name or topic'). It also differentiates this tool from siblings by noting that every other tool needs a slug from this one.

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 positions this tool as the entry point ('Start here: every other tool needs a slug, and this is where a slug comes from'). It also explains that zero matches is a normal answer, not an error. While it doesn't explicitly list when not to use it, the context is clear.

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 disclosure burden. It mentions the message cap, return of the updated thread, and authentication requirements. It doesn't detail rate limits or error handling, but the disclosed cap is a key behavioral trait for a follow-up 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?

Three concise sentences cover purpose, usage guideline, and authentication/details. No redundant information; every sentence adds value. The structure is front-loaded with the main action.

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 3 required parameters, no output schema, and no annotations, the description provides necessary context: purpose, prerequisite (contact_business), cap, and authentication. It could briefly note that message is the content, but that's in schema. Overall adequate.

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 descriptions for each parameter. The description adds value by explaining that token and conversation_id come from a prior contact_business call, providing context beyond the schema. This clarifies the parameter relationships.

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 explicitly states the tool adds a message to an existing conversation opened with contact_business, distinguishing it from sibling tools like contact_business (which opens the conversation) and check_replies. The verb 'add' and resource 'message to conversation' are clear.

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 warns about a message cap per conversation, guiding the agent to send one considered follow-up rather than fragments. It also notes the required authentication and the need for conversation_id and token from contact_business, implying that contact_business should be called first. However, it doesn't explicitly list alternatives or when not to use.

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