SecondAppraisal Total-Loss Intake

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value by clarifying that the tool returns a receipt containing status, a confirm link (while confirmable), and preliminary data. It does not contradict annotations and provides useful behavioral context about the response contents.

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 sentence that front-loads the core action ('Fetch the current receipt') and lists the key contents. It is concise, with no redundant words or filler, and every element contributes to understanding the tool's purpose.

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 (two required parameters, read-only, no output schema). The description explains what is returned (status, confirm link, preliminary) and indicates the confirm link is time-bound ('while still confirmable'). This is sufficient for an agent to know the tool's output shape, though it could clarify the format of the receipt or error scenarios, but given the low complexity, it is nearly 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%: both intake_id and poll_token are documented in the schema. The description adds no new parameter semantics beyond what the schema already states (e.g., that these come from a previous submission). Baseline of 3 is appropriate since the schema carries the full parameter documentation.

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 states a specific verb ('Fetch') and resource ('current receipt for a submitted intake') and enumerates the returned fields (status, confirm link, preliminary). This clearly distinguishes it from sibling tools like get_preliminary_estimate (which fetches estimates) and submit_total_loss_intake (which submits).

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 usage for checking status after a submission, but it does not explicitly state when to use this tool versus alternatives or when not to use it. There are no exclusions or mentions of alternative tools for related tasks (e.g., getting estimates). The context is clear from the name and title, but explicit guidance is absent.

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?

Annotations already cover readOnlyHint, idempotentHint, openWorldHint, and non-destructiveness, so the bar is lower. The description adds valuable context beyond annotations: 'Nothing is stored about the customer' discloses privacy/non-persistence, and 'Not an appraisal' clarifies the output's informal status. No contradiction with the annotations.

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 with the core purpose front-loaded. Each sentence earns its place: purpose and outputs, formal-appraisal caveat, and privacy guarantee. No filler or 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?

Even without an output schema, the description names the expected return components, so the agent knows what it will receive. It does not specify field names, error cases, or fallback behavior when comparables are unavailable, but for a preliminary read-only estimate with safety annotations in place, this is nearly 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 only 38%, so the description must compensate. It adds meaning for key parameters indirectly: 'near the customer' clarifies zip, 'odometer-adjusted' clarifies odometer, and 'gap vs the insurer's offer' clarifies acv_offer_cents. However, it does not clarify trim, state, or the required vehicle identifiers beyond what the schema provides, leaving some compensation gap.

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 states a specific verb and resource: 'a no-commitment early market read for a vehicle' and enumerates concrete outputs (10th percentile/median/90th comparable prices, odometer-adjusted mid, gap vs insurer's offer). It also distinguishes itself with 'Not an appraisal' and, given siblings like submit_total_loss_intake and upload_valuation_report, the agent can clearly tell this is the lightweight read-only estimate tool.

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 clearly positions this as an early, no-commitment read rather than a formal appraisal, giving the agent context for when to invoke it. It does not explicitly name alternatives or state when not to use it, although sibling names such as submit_total_loss_intake make the boundary reasonably inferable.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful content-level context (what facts are returned) but does not disclose additional behavioral details such as error handling or availability of results for all states. No contradiction with annotations.

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 sentence front-loads the core purpose with a colon and then lists specific fact categories without filler or redundancy. Every item in the list 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 one-parameter read-only lookup with no output schema, the description sufficiently explains what the tool returns by enumerating the rule categories. Nothing essential is missing 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?

Schema description coverage is 100%: the single parameter's two-letter code or state-name format is fully documented in the schema. The description does not add parameter semantics beyond the schema, so the 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?

The description clearly identifies the resource as state-specific total-loss rules and enumerates the contents, distinguishing it from the sibling tools (intake status, preliminary estimate, submission, upload). It lacks an explicit verb such as 'gets' or 'looks up', so it stops just short of top score.

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 use case is implied: an agent needing state total-loss facts (threshold, method, appraisal rules, citations, hotline) should call this tool. However, it does not explicitly name alternatives or state when not to use it, leaving the decision to inference.

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?

Annotations declare destructiveHint=true and idempotentHint=true; the description goes further by detailing the receipt contents, contact policy, pricing structure with discounts, free consultation scope, guarantee terms, card hold vs charge, and a strict rule against collecting card details. This is rich context beyond the annotations.

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 content is front-loaded with the core action and minimum requirements, then proceeds to describe outputs and pricing. It is dense but well-organized; however, the pricing section is lengthy and could be trimmed or structured as a list for easier scanning.

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 of the tool, nested schema, lack of output schema, and annotations, the description provides complete information on what happens after submission, including receipt fields, contact policy, and pricing. An agent has everything needed to call it correctly and explain outcomes to a user.

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 all eight parameters and nested fields comprehensively. The description adds some meaning about the receipt and pricing, but does not provide additional parameter-level guidance 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 a specific verb and resource ('Submit a vehicle owner's total-loss situation') and names the receiving service. It is clearly distinct from the read-oriented siblings like get_intake_status and get_preliminary_estimate.

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 lists minimum requirements (one contact, agent self-identification, consent attestation) and explains consent modes via the schema. It does not explicitly say when to use this versus the sibling lookup tools, but the action-oriented nature makes the intent 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?

Beyond the annotations, the description discloses that the tool parses the PDF for specific fields, refreshes the preliminary estimate, and can take up to two minutes. This is meaningful behavioral context that the annotation flags alone do not provide. It does not contradict readOnlyHint or destructiveHint.

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, front-loaded with the action, then the parsing behavior, then the latency warning. Every sentence earns its place and no information is repeated from the schema or title.

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 essential operational facts for a state-changing async upload: what to attach, that a prior submission is required, what parsing and updates occur, and the two-minute wait. The exact response shape is not specified, but the description is sufficient for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents all four parameters with full coverage, so the baseline is 3. The description adds genuine domain context by specifying the accepted report type (CCC / Mitchell PDF) and the data that will be extracted, which helps an agent choose the correct file to attach.

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 first sentence names a specific verb ('Attach'), a specific resource ('Market Valuation Report (CCC / Mitchell PDF)'), and a target ('submitted intake'). The parse-and-refresh detail further distinguishes it from the read-only sibling getters and from the initial submit_total_loss_intake tool.

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 clearly states that the tool applies to an already-submitted intake and implies the sequencing via 'submitted intake' and the need for prior submission tokens. It does not explicitly list exclusions or alternatives, but the context is clear enough for correct selection.

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