MCPFax Vehicle Diagnostics

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

The description discloses that definitions are reworded compilations from open/public sources, that it never guesses, that descriptions are not verbatim, and that severity is not guaranteed. It also notes the cost per call. This is highly transparent, and with no annotations, the full burden is on the description.

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 bit longer than strictly necessary, with six sentences, but each sentence adds value: purpose, output, make-specific usage trigger, no-guess policy, output characteristics, and cost. It is well-structured and not rambling.

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 purpose, the conditions for use, the output nature (reworded definition, source, licence), the no-guess policy, the make-specific distinction, and the cost. Without an output schema, this is sufficient for an agent to understand what the tool returns and how to invoke it.

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 provides complete descriptions for both 'code' and 'make', including the distinction between generic and make-specific meanings. The tool description reinforces this but does not add substantive new information beyond the schema, so it stays at 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 clearly states the tool looks up vehicle fault codes (OBD-II DTCs), which is specific and distinct from sibling tools like recalls or VIN decoding. The verb 'look up' and the resource are 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 explicitly says 'Use this when a scan tool reported a code,' providing a clear trigger. It also gives guidance on passing 'make' for manufacturer-specific definitions. However, it does not explicitly mention alternatives (e.g., when to use vehicle_recalls instead), so it falls short of the highest bar.

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 of behavioral disclosure. It covers ordering (newest first), result limit (max 50), data-source limitations ('ingested NHTSA subset'), the zero-count caveat, and even cost per call. This is unusually 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?

Every sentence carries distinct information: purpose, return fields, query modes, use cases, a caveat, and pricing. It is front-loaded and dense without being verbose.

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?

Despite having no output or annotation schema, the description explains what results will look like, how to query, when to use the tool, what a zero count means, and the cost. It is fully actionable for an agent.

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 four parameters. The description adds useful context about query modes (by make with optional model/year, or by campaign id) but does not provide significant new parameter-level detail beyond what the schema already states.

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 resource ('NHTSA safety recalls for a vehicle') and the exact outputs returned (campaign id, affected component, safety consequence, free remedy). It also distinguishes itself from sibling tools by focusing on recalls rather than DTCs, TSBs, or VIN decoding.

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 gives explicit use cases: before buying a used vehicle or when a customer asks about open recalls. It does not explicitly say when not to use it or name alternatives, but the context is clear enough for an agent to select it appropriately.

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 of behavioral disclosure. It adds key facts: zero count means no match in the ingested subset, a max 50 result limit, and a cost of $0.005 USDC per call via x402. It does not mention authentication or rate limits, but the disclosed behaviors are useful and go beyond what the schema provides.

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 purpose and return contents, then adds usage context, zero-count behavior, and cost. Every sentence contributes information an agent needs; it is slightly long but not bloated. The structure is logical, with the most important facts appearing first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and four parameters, the description fully covers what an agent needs: what is returned (id, summary, fault codes), the max result count, how to query (make/model/year or code), when to use it (after dtc_lookup), and what a zero count means. The cost disclosure also helps with decision-making. Nothing critical is missing for correct 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?

Schema coverage is 100%, so the schema already documents each parameter's type and example. The description adds meaning by clarifying the parameter usage pattern: 'Query by make/model/year, or by fault code' and noting that 'code' is optional and 'make' is required unless code is given, which supplements the schema's flat list. This is above the baseline for fully covered schemas.

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 returns NHTSA Technical Service Bulletins with a specific verb ('Returns') and resource, and distinguishes them from safety recalls. It also names the query methods (make/model/year or fault code), making its purpose unambiguous and distinct from the sibling tools like 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 description explicitly says 'Use this after dtc_lookup when you want the manufacturer's documented fix,' providing a clear condition and sequence. It also explains the query methods (by make/model/year or by fault code). However, it does not explicitly state when NOT to use it or contrast with vehicle_recalls or vin_decode, so it stops short of full exclusion guidance.

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

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

With no annotations provided, the description carries the transparency burden. It discloses the external data source (NHTSA vPIC), the per-call cost of $0.005 USDC, and the payment mechanism (x402 on Base), which are important behavioral facts beyond the tool's name. It does not mention failure modes or invalid VIN handling, but the disclosed cost and external dependency are significant and useful.

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 with no filler: first sentence states the action and output, second gives the use case, third states cost and payment method. It is front-loaded with the core purpose and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter read-only lookup tool, the description covers purpose, output fields, data source, cost, and usage context. The lack of an output schema is mitigated by the explicit list of decoded attributes, and no critical operational detail is missing for correct tool selection and 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 single vin parameter is already fully described in the input schema, including length constraints (11-17 characters), required status, and an example. The description adds no new parameter-level semantics, but because 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 states a specific verb ('Decode'), a precise resource (a VIN), and enumerates the exact output fields (make, model, model year, body class, engine, plant country). It also positions the tool relative to siblings by mentioning it is used 'before looking up recalls or bulletins', making it easy for an agent to distinguish from dtc_lookup, vehicle_recalls, and vehicle_tsbs.

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 explicit guidance: use this tool when you have a VIN and need to identify the exact vehicle before proceeding to recalls or bulletins. It does not explicitly state when not to use it or name alternative tools, but the context strongly implies the appropriate sequence among the siblings.

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