Canada Vehicle Recalls MCP

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

With no annotations, the description carries the full burden. It discloses two useful behavioral traits: the PRO tier marker and the 50-vehicle cap. It does not explain authentication requirements, error behavior for invalid vehicles, or whether results are returned per vehicle, leaving important operational details unstated.

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 definition is a single, front-loaded sentence with no wasted words. The batch limit and vehicle shape are delivered immediately.

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?

There are no annotations and no output schema, so the description should do more to explain what the tool returns and how authentication works. It gives the batch cap and vehicle shape but leaves the response structure and failure modes unspecified, which is a significant gap for a batch API 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 50%, and the description compensates well for the undocumented vehicles parameter by specifying its expected fields ({make, model, year}) and the 50-item cap. The access_token parameter is documented in the schema. It does not clarify which vehicle fields are required individually.

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: checking vehicles for recalls in a batch of up to 50. It also clarifies the expected vehicle shape ({make, model, year}), which helps distinguish it from a single-vehicle lookup. However, it does not explicitly contrast itself with siblings like vehicle_recalls or new_vehicle_recalls.

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 'up to 50 vehicles ... at once' implies this tool is for batch checks rather than single-vehicle queries, but there is no explicit when-to-use guidance or named alternative. An agent must infer that vehicle_recalls is likely the single-vehicle counterpart.

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 behavioral burden. It does not state that this is a read-only operation, does not describe auth requirements beyond the cryptic 'PRO.' marker, and says nothing about pagination, rate limits, or return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is compact and front-loaded with the core scope. The leading 'PRO.' is cryptic, but the rest of the sentence is efficient and avoids filler.

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 annotations, low schema coverage (33%), and no output schema, the description is too sparse. It covers the basic purpose and date format but omits behavioral details, return expectations, and sibling differentiation needed for confident invocation.

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 description adds useful meaning for `since` by specifying the YYYY-MM-DD format and indicates `keyword` is optional. However, it does not clarify what fields the keyword searches, whether the date is inclusive, or how `access_token` is required beyond the schema's own note.

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 (vehicle, tire, and child-seat recalls) and the scope (posted since a date, optional keyword). It is specific enough to distinguish from a detail tool, but it does not explain how it differs from the sibling `vehicle_recalls` or what makes it 'new'.

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?

There is no explicit guidance on when to use this tool versus `vehicle_recalls`, `recall_detail`, or `fleet_check`. The only implied usage is that it retrieves recalls since a given date, leaving the agent to infer routing decisions from names alone.

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 does add real value by disclosing what the response contains (defect, consequence, remedy, units affected) since there is no output schema. However, the cryptic 'PRO.' prefix is never explained — the subscriber-token requirement only surfaces in the schema — and nothing is said about behavior for an unknown recall number, error modes, or rate/quota limits.

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 front-loaded sentence plus a two-character prefix; the purpose and the payload are stated with no filler. The one inefficiency is the unexplained 'PRO.' token, which costs a word without conveying meaning to the agent.

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 two-parameter, no-annotation, no-output-schema tool, the description covers the most important gap — what comes back — which is good. It still leaves the auth/PRO gating, recall-number format, and failure behavior unaddressed, which is thin for a tool with zero structured guidance.

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 only 50%: access_token is documented in the schema, while recall_number has no description at all. The description partially compensates by qualifying it as a Transport Canada recall number, but gives no format or example of what such a number looks like, so an agent still has to guess the expected shape.

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 names a specific verb and resource ('Full Transport Canada recall details for a recall number') and enumerates the returned fields (system, units affected, defect, consequence, remedy). The phrase 'for a recall number' implicitly contrasts it with the list-style siblings (vehicle_recalls, new_vehicle_recalls), though it never names one explicitly.

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?

Usage is only implied: the singular 'a recall number' suggests a follow-up detail lookup after a list tool returns an identifier, and the 'PRO.' prefix hints at a paid/subscriber tier. There is no explicit when-to-use, when-not-to-use, or named alternative among vehicle_recalls, new_vehicle_recalls, or fleet_check.

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 usefully discloses that the source is public/free, that results are capped at 5 newest, and what fields come back (recall numbers and dates). It says nothing about behavior when no recalls match, error conditions, or data freshness, which matter for an agent deciding whether to trust the result.

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 short, front-loaded fragments with no filler — the free/public nature and the 5-result cap are both stated before any caveat. Every clause 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 read-only lookup this covers source, scope, and return contents, but with zero schema descriptions and no annotations the empty-result and required-vs-optional-model ambiguity should have been resolved in the text. The implicit suggestion that model is required is the biggest gap.

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 0%, so the description must compensate; it identifies the three parameters and marks year as optional, but 'make + model (+ optional year)' reads as if model were required when only make is required in the schema. No format guidance (e.g., year as a number, make/model naming conventions) is given.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — Transport Canada safety recall lookup — plus the scoping keys (make + model + optional year). It implies a summary list ('newest 5 with recall numbers and dates'), which distinguishes it from recall_detail, but never names the siblings.

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 'Newest 5' cap implies that recall_detail or new_vehicle_recalls should be used when a full list or detail is needed, but the description never says so explicitly or states when-not-to-use conditions. Usage routing is left to inference.

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