Vehicle Intelligence MCP Server

Server Quality Checklist

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Latest release: v0.3.0
Disambiguation4/5
Each tool has a distinct role: browsing, current lookup, revision history, exact revision, raw observation, and field explanation. The closest pair is lookup_vehicle and get_vehicle_revision, but their current-vs-immutable distinction is clear from descriptions.
Naming Consistency4/5
Names follow a mostly consistent verb_noun snake_case pattern with list_* for collections and get_* for individual records. lookup_vehicle breaks the get_* convention slightly, but it is still readable and predictable.
Tool Count5/5
Six tools is well within the ideal range and each tool addresses a distinct part of vehicle intelligence. The set feels neither thin nor bloated.
Completeness4/5
The read-only workflow is well covered: discover vehicles, retrieve current and historical canonical states, fetch raw evidence, and explain field outcomes. A minor gap is the lack of a way to enumerate source observations for a vehicle directly rather than by known observation ID.
Average 3.6/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 95 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description partially carries the behavioral burden by disclosing newest-first ordering and that only canonical revisions are returned. It does not mention pagination behavior, whether it is read-only, or how revisions relate to other vehicle data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that conveys the action, object, and ordering without filler. It earns its place and is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The core operation is clear, but the definition is incomplete for correct invocation because it omits how limit and before_revision should be used. The output schema helps with return values, but not with parameter semantics or pagination strategy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description adds no parameter information. 'vin' and 'limit' are somewhat self-explanatory, but 'before_revision' lacks crucial semantics such as whether it is a cursor, whether it is inclusive, or what revision identifier it expects.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: retrieve historical canonical revisions, and adds a meaningful ordering detail (newest-first). The word 'historical' and the plural 'revisions' distinguish it from the sibling get_vehicle_revision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus get_vehicle_revision, get_source_observation, or lookup_vehicle. The context is only implied by the phrase 'historical canonical revisions', not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It adds useful traits like 'bounded page' and 'canonical' summaries, implying a read-only, paged listing of normalized data. It does not state side effects, ordering, error behavior, or pagination details, but the read-only nature is reasonably inferable from 'List'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence with no filler. It front-loads the action and resource, and every phrase contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two optional parameters and an output schema, so core invocation details are mostly covered. Missing context includes how this listing relates to sibling tools like lookup_vehicle and whether any pagination defaults or ordering behavior are important. It is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explicitly explain how limit and offset control the 'bounded page.' The parameter names and defaults communicate some meaning, but the description fails to compensate for the lack of schema-level documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and identifies the resource ('canonical vehicle summaries from the catalog'), clearly indicating the tool returns a paged collection of summary data. It does not explicitly differentiate itself from siblings like lookup_vehicle, though 'bounded page' suggests a listing operation rather than a single lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for discovery' implies this tool is meant for browsing or exploring the catalog, which gives some usage context. However, there is no explicit statement about when to use this tool versus lookup_vehicle or any other sibling, and no exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context by specifying 'current evidence' and 'explain' implies a read-only operation, but it does not state side effects, permissions, or expected output characteristics. The output schema may compensate slightly, but the description alone is incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It states the action, the resource, and the outcome set immediately, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with an output schema, the description is mostly complete. It clearly identifies the tool's purpose, but the lack of valid field_name values and the absence of guidance on when to prefer this over sibling tools leave minor gaps. Overall, an agent could likely invoke the tool correctly with minimal extra inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the schema's sparse parameter documentation. It indirectly references field_name via 'vehicle field outcome' but does not explain what field_name values are valid, what vin means beyond its obvious name, or how the two parameters interact. The description adds minimal parameter-level meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Explain') and resource ('one vehicle field outcome'), and enumerates the possible outcomes (RESOLVED, UNRESOLVED, ABSENT), which gives clear scope. It is distinguishable from sibling tools like get_vehicle_history or lookup_vehicle, though it does not explicitly name them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for explaining a single field's outcome using evidence, and sibling names provide nearby alternatives, but there is no explicit when-to-use or when-not-to-use guidance. An agent can infer the intended use, but must reason about alternatives itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 adds useful behavioral context: the observation is immutable, the retrieval is exact, and the response includes the verified raw payload. It does not explicitly state read-only, but the verb 'Retrieve' makes that clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler. It communicates the action, the exactness, the immutability, and the payload content efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter lookup with an output schema, the description covers the core semantics adequately. It lacks guidance on when this tool should be chosen over sibling tools, which prevents a perfect score, but nothing critical is missing for invoking it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only a parameter name and no description (0% coverage), so the description must compensate. The phrase 'by ID' tells the agent that observation_id is the identifier, but it provides no format, origin, or additional constraints beyond the schema's required flag.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is specific: 'Retrieve one exact immutable source observation by ID' names the verb, the resource, and the lookup key, and adds 'including verified raw payload' to clarify what is returned. It does not explicitly differentiate from sibling tools like get_vehicle_revision, but the resource is distinct enough to be clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for retrieving a single known observation by its ID, but it never states when to prefer it over siblings such as get_vehicle_history or get_vehicle_revision, nor does it mention any exclusions or alternative conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 makes the read-only nature clear through 'Retrieve' and specifies the response concept (canonical record plus audit metadata), but it does not disclose behavior for missing/invalid VINs, the nature of audit metadata, or any validation effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one focused sentence with no filler. It front-loads the action and object and avoids repeating schema information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with an output schema, the description covers the essential purpose and return concept. It does not cover error cases or alternative selection, but those gaps are relatively minor given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add parameter meaning. It contributes 'validated' to the VIN parameter and clarifies it identifies a single vehicle, but it does not provide format, length, or validation requirements. The parameter name is self-explanatory enough to reach the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Retrieve'), a specific resource ('current canonical record'), and a scope ('one validated VIN'). It clearly differentiates from siblings like get_vehicle_history and get_vehicle_revision by emphasizing current canonical state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'current canonical record' implies this tool is for the latest authoritative state rather than history or source observations, but it never explicitly states when to use this tool over alternatives such as get_vehicle_history, get_vehicle_revision, or list_vehicles. Usage guidance is only implied by the wording.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 meaningfully discloses that the revision is immutable, canonical, and singular, which is valuable context beyond the operation name. It does not mention behavior for missing revisions or authentication, but for a simple read-by-revision getter the core behavior is clearly conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence with no filler. It front-loads the action and resource and includes meaningful qualifiers ('exact,' 'immutable,' 'canonical') that clarify behavior without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a low-complexity tool with two scalar required parameters and an output schema present, so the definition does not need to explain return shape. The description adequately captures the core semantics of retrieving a single canonical immutable revision. It would be slightly stronger with explicit guidance on how to know the revision_number or what happens if the revision does not exist.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only elaborates 'revision number' and does not explain that the vehicle is identified by 'vin' or clarify the exact expected semantics of revision_number (e.g., uniqueness, ordering, or range). The parameter names are self-explanatory, but the description adds little meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Retrieve'), names the exact resource ('canonical revision for a vehicle'), and adds the qualifiers 'one exact immutable,' which clearly differentiates this tool from siblings like get_vehicle_history or list_vehicles. An agent can understand the tool's purpose without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when the caller has a specific revision_number and needs the exact canonical revision, but it does not explicitly state when to prefer sibling tools such as get_vehicle_history or when not to use this tool. The usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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