marketplace

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

The description lacks behavioral details beyond the basic read operation. It claims to get 'all' vehicles but the presence of limit/offset suggests pagination, which is not disclosed. No error handling or response format is mentioned, and annotations are absent, so the description carries the full burden.

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, concise sentence with no wasted words. It is front-loaded with the purpose and is appropriately brief, though it lacks detail.

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, no annotations, and only 33% schema coverage, the description is insufficient. It does not specify the return type, pagination semantics, or error scenarios, making it incomplete for an agent to fully understand the tool's behavior.

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?

Only one of three parameters (dealer_id) has a schema description, and the tool description does not explain limit or offset. The description adds minimal value by implying that dealer_id identifies the dealer, but it does not clarify pagination defaults or usage.

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's function: retrieving all vehicles at a specific dealer. It uses a specific verb ('Get') and resource ('vehicles at a specific dealer'), distinguishing it from sibling tools like cars_dealers and cars_vehicle.

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 no guidance on when to use this tool versus alternatives like cars_search. There is no mention of exclusions, prerequisites, or context for choosing this tool.

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 present, so the description must convey behavior. It only states the query parameters, not outcomes like pagination, result format, or read-only nature. This under-disclosure scores a 2.

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?

A single, clear sentence with no redundant wording—excellent conciseness.

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 simple search tool, the description is adequate but lacks details on return structure or result behavior, which matters since no output schema is provided. Thus a 3.

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?

All params have schema descriptions, so the tool description adds no extra semantic value beyond the schema. The baseline 3 applies.

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 identifies the tool's function—searching for car dealers—and specifies the search criteria (city, state, ZIP), distinguishing it from sibling tools like cars_search or cars_dealer_inventory.

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?

It provides clear context for when to use the tool (location-based dealer lookup), but does not explicitly mention alternatives or exclusion scenarios, which keeps it at a 4.

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 adds that 'We actively read every piece of feedback,' which is non-obvious, but does not explain persistence, side effects, or response 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 three sentences and front-loaded with the action. The final sentence is slightly redundant with the second, but overall it is compact and 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?

For a simple feedback tool with no output schema and full parameter descriptions, the description adequately explains the purpose and content guidance. It lacks information about return values, but this is not critical for this 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?

The schema covers all six parameters with descriptions, so the baseline is 3. The description reinforces the 'user_happy' concept and mentions 'what's missing' which aligns with the 'message' parameter, but it does not add significant new meaning beyond 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 opens with 'Submit feedback about the Cars Rootz service,' using a specific verb and resource. It clearly distinguishes this from sibling tools like cars_search or cars_session_create by focusing on feedback submission.

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?

It explains the tool's purpose (submitting feedback) and encourages the agent to describe what worked, what didn't, and user happiness. However, it does not explicitly state when to use it over alternatives or include any exclusions.

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 response scope (full history, dealer appearances, price changes, etc.) and adds 'Data with origin' suggesting provenance. The verb 'Get' implies a read-only operation, and no side effects are indicated. However, it does not mention rate limits, authentication, or potential response limitations.

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, information-dense sentence that front-loads the action ('Get the full history') and then lists specific data points. Every part adds value, with no redundant or filler content. The trailing 'Data with origin' is terse but informative.

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 single-parameter lookup with no output schema, the description sufficiently explains what the tool does and what the response contains (dealer appearances, price changes, days on market, resurfacing). It does not detail return structure or edge cases, but the core purpose and output are clear enough for an agent to invoke it 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 schema description coverage is 100%: the single 'vin' parameter is described as '17-character Vehicle Identification Number'. The description adds no additional parameter semantics or format details beyond what the schema already provides, so the 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 specific action and resource: 'Get the full history of a vehicle by VIN'. It enumerates the exact data included: dealer appearances, price changes, days on market, and whether it sold and resurfaced, which differentiates it from sibling tools like cars_vehicle that likely provide current vehicle info.

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 implies its use for historical vehicle data ('full history') but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among sibling tools. Usage is inferred from the content rather than explicitly stated.

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 the burden of behavioral disclosure. It states the action and return, which is helpful, but does not disclose details like result ordering, how filters combine (AND/OR), inclusivity of min/max values, or any authentication/rate-limit requirements. Lacks depth but is not misleading.

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 redundancy. The first sentence states the core function and scope, the second lists filter dimensions and return value. Every word contributes to understanding.

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 tool has 15 parameters, no required fields, and no output schema. The description adequately conveys the high-level purpose but leaves gaps such as default sorting, whether location refers to dealer or vehicle location, and what 'dealer info' includes. Given the complexity, a complete description would provide more operational context, but the core is covered.

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 covers 100% of the parameters with descriptions, so the baseline is 3. The description's mention of filter categories (make, model, etc.) adds little beyond the schema, and it does not explain parameter interactions or default behaviors that the schema omits.

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 a specific verb ('search'), the resource ('used car inventory'), and the scope ('across all dealers'). It also lists the filter dimensions and the return value ('matching vehicles with dealer info'), distinguishing it from sibling tools like cars_dealer_inventory or cars_vehicle.

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 phrase 'across all dealers' implies a broad search use case, which contextualizes when to use it. However, it does not explicitly mention alternatives or when not to use it (e.g., when searching within a specific dealer, use cars_dealer_inventory). The usage guidance is implied rather than explicit.

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 disclose side effects. It only says 'Add' and gives parameter guidance, without mentioning whether this modifies session state, requires permissions, is reversible, or what the response/return value looks like. For a mutating tool this is a notable gap.

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 verb and object, and no redundant or vague content. It directly tells the agent what to do and what to include.

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 low-complexity add operation with full schema coverage, the description is nearly sufficient. However, it lacks any mention of output, success/failure behavior, or session state consequences, which would be more important because no output schema or annotations exist.

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 input schema already documents all five parameters. The description adds no new meaning beyond restating score (1-10) and notes (why you recommend), which matches the schema descriptions. Baseline of 3 applies.

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?

Description uses a specific verb ('Add') and resource ('a vehicle to the buyer's shopping session'), clearly distinguishing it from sibling tools like compare, interest, or tradein. It also mentions the key inputs (fit score, notes) that make the tool's purpose unmistakable.

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 gives clear context: use when adding a recommended vehicle to an active buyer session. It does not explicitly name alternatives or exclusions, but the action is unambiguous and distinct from the sibling session tools.

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 the full burden. It implies read-only behavior via 'Get' and discloses the output components (specs, pricing, market context). However, it does not explicitly state that this is a safe, non-mutating operation or disclose any potential side effects, which leaves a moderate gap.

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, front-loaded with the core function, and includes a brief value statement. Every word earns its place with no redundancy or fluff.

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 simple one-parameter tool with no output schema, the description adequately explains what the tool does and what output to expect (specs, pricing, market context). While it leaves room for ambiguity around 'market context', it is sufficiently complete 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?

The schema already provides a clear description for the sole parameter ('Session hash'), achieving 100% coverage. The tool description adds no additional detail about the parameter, but because the schema covers it, the baseline 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 uses a specific verb 'Get' and clearly identifies the resource ('structured comparison of all vehicles tracked in a session'). It distinguishes the tool from siblings like cars_session_read or cars_session_add_car by focusing on side-by-side comparison of specs, pricing, and market context.

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 clear usage context: 'Great for helping the buyer decide' implies a decision-support scenario. While it does not explicitly name alternatives or exclusions, the use case is evident from the sibling tool set, so it earns above baseline.

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 key behaviors: creates a session, saves buyer situation (payment, trade-in, timeline), returns a bookmarkable URL, and emphasizes natural data gathering to strengthen the dealer lead. It does not mention potential side effects like overwriting existing sessions, but for a create tool this is sufficient. The added context about the dealer lead goes beyond basic functionality.

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 focused paragraph with a clear lead sentence about the core action, followed by supporting behavioral guidance. All sentences earn their place: the URL return value, the benefit to the dealer lead, and the advice to gather information naturally. It is not excessively verbose, though it could be slightly more concise by trimming redundant phrasing.

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 11 optional parameters and no output schema, the description gives enough operational context: what the session is for, what to save, and the key output (URL). It does not explain how to handle existing sessions or duplicates, but for a create tool this is acceptable. The presence of sibling tools like cars_session_tradein is mentioned in the schema, and the description complements that by setting the session-level 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?

The input schema covers 100% of parameters with detailed descriptions, so the baseline is 3. The description mentions payment method, trade-in, and timeline as things to save, which maps to those params, but adds no additional parameter-specific semantics beyond what the schema already provides. The general guidance to gather naturally is useful but does not explain any particular parameter value or format.

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 verb and resource: 'Create a personalized car research session for the buyer.' It also distinguishes the tool by noting it returns a unique URL to bookmark and return to, which is a distinctive feature among sibling session tools. The purpose is specific 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?

The description provides usage context by saying 'As you help the buyer' and guides on how to collect information: 'Don't ask all at once — gather these naturally over the conversation.' This gives clear timing and behavior, though it doesn't explicitly contrast with sibling tools like cars_session_read or cars_session_add_car. The instruction to not interrogate helps the AI agent understand when and how to use this tool.

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 the full burden. It uses 'Get' to imply a safe, read-only operation and enumerates the returned data (offers, messages, unread count), indicating no side effects. It doesn't disclose edge cases or error conditions, but for a simple status getter this is adequate.

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 one compact sentence with a hyphen-separated list of contents, followed by a clear use case. Every phrase adds value and the most important action ('Get') is 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?

The tool is simple with one parameter and no output schema, so the description doesn't need to be verbose. It specifies the core outputs (offers, messages, unread count) and the intended use case. It could mention that it requires an existing session, but the schema already states that; overall it is sufficiently complete for a tool of this 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?

The schema fully documents the single 'session' parameter as a 'Session hash' (100% coverage). The description does not add extra parameter context, but the baseline of 3 applies because the schema is self-sufficient.

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 verb 'Get', the resource 'current deal status', and specifics: offers received, messages exchanged, unread count. This distinguishes it from sibling tools like cars_session_message or cars_session_reply, which focus on messaging rather than overall deal status.

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 check if the dealer has responded,' providing a clear when-to-use scenario. However, it lacks exclusions or mention of alternative tools, so it doesn't fully differentiate in both directions.

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 the full burden of behavioral disclosure. It clearly states that the tool sends a professional email on the buyer's behalf, which is a key side effect. However, it does not disclose other potential behaviors such as data persistence, reversibility, or authorization requirements.

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 exceptionally concise, consisting of two sentences that front-load the action ('Signal buyer interest') and follow with necessary context. Every word earns its place with no 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?

For a simple tool with no output schema, the description adequately covers the primary purpose, the mechanism (email), and a usage condition. It is complete enough for an agent to understand what the tool does and when to invoke it, though it could optionally mention the response or confirmation behavior.

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 provides descriptions for all 4 parameters, achieving 100% schema description coverage. The description does not add additional meaning beyond the schema, so the 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's purpose: 'Signal buyer interest in a vehicle to the dealer' with the specific action of sending a professional email. It distinguishes itself from sibling tools like cars_session_message by focusing on interest signaling rather than general messaging.

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 explicit usage guidance: 'Only use when the buyer has explicitly indicated interest.' This is a clear condition for when to use the tool, though it does not explicitly name alternatives or contrast with sibling tools.

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 the full burden. It discloses that the message is visible to the dealer and other AI agents, which is useful behavioral context. It also clearly implies a write operation. However, it does not mention any potential side effects, such as whether the message is appended or if there are permission requirements. This is adequate for a simple messaging tool 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 three concise sentences, each adding value: the action, the use cases, and the visibility. It is front-loaded and free of fluff.

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 tool is simple, the schema covers all parameters, and the description provides purpose and use cases. No output schema exists, so return values need not be explained. The description is complete enough for an agent to use the tool correctly, though it could mention potential limitations or side effects.

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%, meaning every parameter already has a description in the input schema. The tool description adds no additional parameter-level detail. Baseline for high coverage is 3, which 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's function with a specific verb and resource: 'Post a message to the session.' It also gives example use cases. However, it does not explicitly differentiate from sibling tools like cars_session_reply, leaving some ambiguity about when to post vs. reply.

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 explicit use cases: 'Use for buyer questions, AI analysis notes, or responses.' This indicates when the tool is appropriate. It does not mention when not to use it or alternatives, but the guidance is clear enough for an AI agent to make a reasonable decision.

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 transparency burden. It goes beyond a simple setter by explaining the implications of each mode (e.g., dealer never sees buyer email in bcc) and the side effect of upgrading the session identity to 'email'. It does not discuss error conditions or overwrite behavior, but the added context is meaningful.

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, front-loaded with the purpose, and efficiently explains the three modes without redundancy. Every sentence contributes useful 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 complexity and the lack of an output schema, the description covers the core behavior and mode semantics well. It does not mention return values or failure scenarios, but for a preference-setting tool this is a minor gap, and the available schema provides parameter-level details.

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 semantic depth to the 'mode' parameter by explaining the real-world consequences of bcc/cc/none, which is not fully captured in the schema's short descriptions. Other parameters like 'session' and 'email' are straightforward.

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 uses a specific verb and resource: 'Set the buyer's email notification preference for this session' and details three concrete modes. It clearly distinguishes from sibling tools focused on session creation, reading, or messaging by centering on notification configuration.

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 usage context is implied through the description of the action and modes, but there is no explicit statement of when to use this tool versus alternatives or any exclusion criteria. The 'when' is obvious but not directly articulated.

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 burden. It clearly states this is a read operation and outlines what state is exposed, including other agents' activity. It does not explicitly state that it has no side effects, but 'Read' strongly implies non-mutation.

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 concise sentences: first defines purpose and scope, second gives direct usage advice. No filler, all information 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?

The tool is a simple read operation with 2 well-documented parameters. The description enumerates the key data categories returned, which is sufficient for an agent to understand what context it will obtain, even 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%, so the schema already documents the session hash and agent_id. The description adds no additional parameter-specific detail beyond what the schema provides, matching the 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 uses a specific verb ('Read') and clearly identifies the resource ('current state of a shopping session') plus a list of contained elements. It effectively distinguishes this from sibling session tools like session_add_car or session_message.

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?

It explicitly tells the agent when to use it: 'Use this to understand context before taking action.' This is clear guidance, though it does not mention when not to use it or name alternative tools.

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 burden of behavioral disclosure. It adds useful context that the email thread continues naturally and prerequisites (after dealer reply), but it does not discuss side effects, irreversibility, or failure modes.

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, front-loaded with the action ('Send a follow-up message') and followed by concise usage guidance. 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 simple message-sending tool with no output schema, the description adequately covers what it does and when to use it. It could mention application state requirements more explicitly, but given the schema richness, it 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?

The input schema already describes all four parameters (vin, message, session, agent_id) with 100% coverage, so the description adds no additional parameter semantics beyond what the schema provides.

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 sends a follow-up message to the dealer, with specific use cases (negotiate, ask questions, schedule visit). It distinguishes itself from sibling tools by emphasizing 'follow-up' and 'continuing the conversation.'

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?

It explicitly says to use this after the dealer has replied and the buyer wants to respond, providing clear timing. However, it does not explicitly name alternative tools or state when not to use it, though the 'follow-up' language implies it is not for initial messages.

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 transparency burden. It does disclose that the tool supports progressive updates ('You don't need everything at once') and implies a partial update model. It also mentions the dealer uses this info for the offer. But it does not state whether the operation overwrites existing fields, whether it creates or updates a record, or any potential side effects like changes to the deal. This is moderate transparency for a mutation 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?

The description is concise (about 55 words) and front-loaded with the action and resource. It packs essential guidance into three sentences without redundancy. Each sentence adds either purpose, usage strategy, or tiering 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 complexity (19 parameters) and lack of output schema/annotations, the description is largely complete: it explains what to collect, how to approach it progressively, and the tiers. The only gap is that it mentions 'photos via the bridge page' but the schema has no photo parameter, which may be slightly confusing. It also does not describe the return value, but that is secondary for an update 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?

The schema covers all 19 parameters with descriptions, so the baseline is 3. The tool description adds value by grouping parameters into tiers (quick = VIN/plate + mileage, standard = condition answers, full = photos via bridge page), which helps the agent decide which optional parameters to include. This semantic grouping goes beyond the flat schema descriptions.

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 opens with a specific verb+resource: 'Set or update the buyer's trade-in vehicle information.' This clearly distinguishes the tool from its sibling cars_session_tradein_read, which is for reading. It also defines the scope (trade-in vehicle) and the goal (dealer makes offer based on this info).

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 gives clear usage context: gather trade-in info from the conversation, start with what the buyer knows, and build progressively. It also defines three tiers (quick/standard/full) that guide how much info to collect. However, it does not explicitly mention when to avoid this tool or point to alternatives, so it stops short of a 5.

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 and does disclose the main returned components and the extra photo_upload_url behavior. However, it does not discuss permissions, error behavior, or whether generating the upload URL has side effects, leaving some behavioral context unclear.

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 efficiently deliver the action, output contents, and the photo-upload URL behavior. The description is front-loaded with the verb and resource, with no unnecessary 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?

Since there is no output schema, the description appropriately enumerates the returned items (decoded vehicle info, condition, photos, summary, photo_upload_url) and adds a practical use case. It is complete for a simple one-parameter read tool, though sibling differentiation is left implicit.

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 only required parameter, session, is already fully documented in the schema as 'Session hash', providing 100% coverage. The description adds little beyond the schema, only implicitly connecting the session to the buyer's trade-in context.

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?

Description opens with the specific action 'Read the buyer's trade-in vehicle profile', clearly identifying the resource and distinguishing it from sibling tools like cars_session_tradein or cars_session_read. The returned fields make the tool's function 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?

The description mentions a practical use case ('suitable for including in dealer communications') but does not explicitly state when to use this tool versus alternatives or provide exclusions. Guidance remains implicit rather than direct.

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, and it goes well beyond a bare 'schedule' by disclosing that it generates a visit code and QR code, that this code proves the AI-buyer connection to the dealer, and that it enables the $100-$200 incentive. It does not mention side effects like dealer notifications or persistence, but the disclosed behavior is substantial and accurate. No contradiction.

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 four sentences, front-loaded with the core action, then explaining outputs and usage. The third sentence ('This is how the dealer knows...') and fourth condition are useful, though 'proves the connection' is slightly redundant with the prior sentence. Overall efficient and well-structured.

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 does explain the key outputs (visit code and QR code) and the required precondition. It doesn't enumerate optional parameters, failure modes, or return format, but the 100%-coverage input schema handles parameter semantics. This is enough for an agent to select and invoke correctly, though a slight gap around dealer notification or return details prevents a 5.

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 baseline is 3. The description mentions 'test drive, purchase, trade-in appraisal,' which loosely mirrors the visit_type parameter values, but it does not add detail beyond the schema's own property descriptions. It provides no extra parameter-specific semantics, so 3.

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?

Description opens with a specific verb-object pair, 'Schedule a dealer visit,' and enumerates the visit types (test drive, purchase, trade-in appraisal), which distinguishes it from sibling session tools like message/deal. It also uniquely identifies the tool's output (visit code and QR code), making the purpose unmistakable.

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 an explicit triggering condition: 'Only use when the buyer explicitly says they want to visit the dealer.' It does not explicitly name alternative tools, but the clear focus on dealer visits implies when this should be used over session messaging or deal tools. No explicit exclusions beyond the condition, so 4.

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 the full burden. The word 'Get' clearly implies a read-only operation with no side effects, and the description accurately describes the statistical nature. However, it does not disclose return format, potential limitations, or any required permissions, which would be valuable context.

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, concise sentence that front-loads the tool's action ('Get database statistics') and provides concrete examples without redundancy. Every word 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?

For a simple, parameterless tool without an output schema, the description adequately conveys the returned statistics. It lists the key aggregates, which gives a clear expectation of the result. Minor gap: does not specify the format (e.g., a single object, typed values), but overall sufficient for the tool's simplicity.

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 tool has zero parameters, so there is no schema burden. The description lists the statistics categories, which effectively communicates what the output will contain. Baseline of 4 for zero-parameter tools 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 verb 'Get' and resource 'database statistics', and enumerates specific statistics (total vehicles, dealers, coverage by state, top makes). This distinguishes it from sibling tools like cars_search or cars_vehicle, which focus on specific actions rather than aggregate stats.

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 does not provide any guidance on when to use this tool versus alternatives. No exclusions or contextual triggers are mentioned, leaving the agent to infer usage solely from the name and general purpose.

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 the burden of disclosing behavior. It explicitly lists the types of data returned (specs, price, mileage, photos, recalls, dealer info), giving useful transparency beyond the schema. However, it does not mention edge cases like invalid VINs or missing vehicles.

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, well-structured sentence that front-loads the core purpose and immediately lists key return categories. No wasted words or 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?

For a simple one-parameter lookup tool without an output schema, the description provides a good overview of expected results. It covers the core return categories, though it omits potential error behavior and any prerequisites like VIN validity.

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 schema fully documents the single parameter 'vin' with a description ('17-character Vehicle Identification Number'), so the description adds little beyond confirming the lookup is by VIN. Since schema coverage is 100%, the baseline 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 uses a specific verb ('Get') with a clear resource ('full details for a specific vehicle by VIN') and enumerates the return content. This clearly distinguishes it from sibling tools like cars_search or cars_history.

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 implies the tool is for retrieving details when a VIN is known, but it does not explicitly state when to use it versus alternatives such as cars_search for finding vehicles or cars_history for historical data. No exclusions or alternatives are named.

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