vindex-mcp

Server Quality Checklist

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Latest release: v0.2.2
Disambiguation5/5
Each tool serves a clearly distinct purpose: health, pricing, sample, decode, recalls, costs, and known issues. There is no overlap in primary functionality, and even the bundled data in get_known_issues does not conflict with the dedicated tools.
Naming Consistency5/5
Tool names follow a consistent verb_noun snake_case pattern. Most use the get_ prefix, and decode_vin uses a different verb but still follows the same grammatical structure, making the set predictable and easy to navigate.
Tool Count5/5
With 7 tools, the server is well-scoped for a VIN data API: 3 free discovery tools and 4 paid data endpoints. Each tool earns its place and the count is within the ideal 3-15 range.
Completeness5/5
The tool surface covers the full lifecycle of the VIN data domain: discovery (pricing, sample, health), core decoding, recalls, purchase costs, and known issues. No obvious gaps exist, as the sample tool explicitly enumerates the four paid endpoint shapes, all of which are implemented.
Average 4.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does an excellent job: it details the NHTSA vPIC source, the warranty scope keyed to make/model-year, that it is NOT remaining warranty, cost per call, USDC payment on Base via x402, compute-first settle-after policy (never billed on error), and the need for EVM_PRIVATE_KEY in live mode. This is far beyond what a typical description provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and then provides essential details in a dense, structured manner. It is longer than two sentences, but every clause adds necessary value—warranty caveats, cost, billing model, and auth requirements—so it avoids useless filler while remaining compact for the amount of information conveyed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers all the critical context: what is decoded, the warranty block origin, exclusions, cost, payment path, compute-first error handling, and auth requirements. For a single-parameter tool, this is highly complete and equips an agent to invoke it correctly and interpret results.
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 100% for the single 'vin' parameter, which already includes a 17-character format and an example. The description does not add extra semantic detail about the parameter, so the baseline of 3 is appropriate.
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?
Uses the specific verb 'Decode' with a clear resource ('17-char VIN via NHTSA vPIC') and enumerates the exact output fields (make/model/year/engine/body/drive plus warranty block). This clearly distinguishes it from sibling tools like get_recalls, get_pricing, and get_health.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly communicates what the tool does and explicitly excludes recalls, extended warranties, and emissions warranties, which helps an agent know when NOT to use it. It does not explicitly name alternative sibling tools, but the exclusions imply that get_recalls would be for recall data, making guidance clear.
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 burden. It discloses that the tool requires no wallet/API key and that it reports the payment mode (live|mock), which is useful behavioral context. It does not describe response format or error behavior, but for a simple health check, this is acceptable.
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 sentence, front-loaded with the core purpose ('Check Vindex API health and configured payment mode') followed by the caveat about being free and requiring no credentials. No wasted words.
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 zero-parameter health check, the description covers the main purpose, the specific items checked (health and payment mode), and the authentication requirement. It omits response format details, but since there is no output schema, the description is largely complete for its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so parameter semantics are inherently N/A. Per the scoring rules, a baseline of 4 is appropriate when there are no parameters to explain.
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 'Check' as a specific verb, identifies the resource as 'Vindex API health', and adds 'configured payment mode (live|mock)', which clearly distinguishes this tool from siblings like get_pricing or decode_vin.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It states 'Free, no wallet or API key required', clearly indicating when the tool can be used without prior setup. However, it does not explicitly mention alternatives or conditions when not to use it, so it falls 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: cost of $0.05, payment via USDC on Base through x402, compute-first billing preventing charges for errors, refusal below 15 complaints with free partial data, and the requirement for EVM_PRIVATE_KEY in live mode. It even discloses hallucination-gating and complaint citation verification.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph but every sentence adds value, explaining outputs, costs, refund logic, and requirements. It is longer than average but justifiably so given the complexity; a bulleted format could improve scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all major aspects: the main outputs, the bundled reliability block, recall counts, decoded vehicle, sample size, cost, payment method, refund policy, threshold behavior, and key requirements. Even without an output schema, it gives enough detail for an agent to understand what will be returned and what side effects may occur.
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 already provides a clear description of the only parameter (vin) with an example. The tool description does not add further semantic detail about the parameter beyond what the schema offers, so the score is at 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 uses a specific verb (get) and resource (known failure modes for a VIN) and clearly differentiates the tool from siblings by detailing its bundled reliability aggregates, recall counts, and decoded vehicle. It is unambiguous about what the tool returns and its grounding in NHTSA ODI complaints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for VIN-level failure modes, reliability aggregates, recall counts, and a decoded vehicle. It mentions the 15-complaint threshold and free partial return, but does not explicitly state when to prefer alternatives like get_recalls or decode_vin, so it falls 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses key behavioral traits: cost per call, payment method via x402, compute-first settlement that prevents billing for errors, and the requirement for EVM_PRIVATE_KEY in live mode. This goes far beyond what typical descriptions offer and gives the agent critical operational understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by cost, payment, billing behavior, and requirements. Each sentence adds unique value, though the length (5 sentences) is slightly more than needed for such a simple tool. No wasted words, but it could be tightened without losing critical info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description is remarkably complete. It covers the data source, licensing/credential requirements, cost model, payment flow, and failure policy. It even hints at return content ('full decoded vehicle folded in'). This is more than sufficient for an agent to invoke the tool 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 input schema already describes the only parameter (vin) with a clear example. The description adds no additional parameter-level detail beyond implying the VIN is used for the recall lookup. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The first sentence clearly states the tool retrieves merged US (NHTSA) and Canada (Transport Canada) recalls for a VIN, with the decoded vehicle info included. It distinguishes itself from siblings by claiming to be the only API merging both datasets, and the mention of decoded vehicle separates it from decode_vin.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes clear when to use this tool: when you need recalls for a VIN, especially cross-border coverage. It does not explicitly mention alternative tools for other use cases, but the context is strong enough that an agent would know this is the recalls tool. Prerequisites like EVM_PRIVATE_KEY and live mode are provided.
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. It discloses the fixed sample vehicle, the live computation, and the free/no-auth behavior. It also elaborates on what each kind includes, which is helpful for setting expectations. It doesn't mention rate limits or error behavior, but for a sample endpoint that's acceptable.
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 three sentences, front-loaded with the core value proposition. The parameter details are compact but informative, and there is no redundancy.
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?
The tool is simple with one parameter and no output schema. The description covers purpose, usage, and parameter specifics, and even hints at response shape composition. For a sample-preview tool, this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the 'kind' parameter with an enum, so baseline is 3. The description adds semantic context for each enum value, such as 'decode (incl. factory-warranty block)' and 'purchase-costs (returns both CA + US),' which goes beyond the schema. This enrichment justifies a higher score.
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 clearly states the tool's function: fetching a free sample response for paid endpoint shapes, using a fixed sample vehicle. It distinguishes itself from siblings like decode_vin or get_recalls by explicitly noting it's a sample before paying. The verb+resource+scope is specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use this to see the exact response shape before paying,' giving clear when-to-use context. It also states it's free and requires no wallet or API key, which implies it's for evaluation rather than production. However, it doesn't explicitly name the alternative tools for actual data, leaving some ambiguity.
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 discloses that the call is free and requires no wallet or API key, which is important behavioral context. It also identifies the HTTP GET method. However, it does not mention rate limits, response size, or whether the response is paginated, which would add further transparency.
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 three sentences, each earning its place: the first defines the resource and contents, the second gives the usage scenario, and the third states the auth/access requirements. It is front-loaded with the action and resource, and contains no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a discovery tool with no parameters and no output schema, the description is remarkably complete. It covers the exact data returned (endpoints, parameters, prices, network, sources, attribution), the intent (discover what you can buy), and the authentication requirements (none). This fully equips 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there are no parameter details to explain. The description compensates by clearly describing what the tool returns. Baseline for a no-parameter tool is 4, and the description exceeds that by detailing the output contents.
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 the specific verb 'Get' and clearly identifies the resource as the Vindex endpoint catalog with per-call USD prices. It explicitly names the endpoint path (GET /discovery) and lists the contents (paid endpoints, params, prices, network, sources, attribution), which distinguishes it from siblings like get_health or decode_vin.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case: 'Use this to discover what you can buy and what it costs.' This tells the agent when to invoke the tool. It does not explicitly compare to alternatives like get_purchase_costs, but the context of discovery/pricing is distinct enough that no exclusion is necessary. The lack of explicit alternative naming prevents a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It clearly states per-call cost ($0.02), payment mechanism (USDC on Base via x402), compute-first/settle-after billing (never billed for errors), and the need for a private key in live mode. It also hints at output structure (per-line sourceUrl + confidence). This is exemplary transparency for a paid API tool.
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 dense but every sentence contributes value: purpose, jurisdiction breakdown, costing, settlement model, and requirements. It is front-loaded with the core purpose and structured by CA/US sections. No fluff or repetition, making it efficient for an AI agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description must convey return shape; it does so by noting 'per-line sourceUrl + confidence' and itemizing fee categories. It also covers coverage (all provinces/territories and states), usage constraints, and billing behavior. For a 9-parameter tool, this is a complete picture.
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 100%, so the baseline is 3. The tool description does not add parameter-level semantics beyond what the schema already provides; it only summarizes jurisdiction coverage and fee categories. No additional meaning is imparted for parameters like state, province, or sale_type beyond their schema descriptions.
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 opens with 'Itemized government-imposed used-vehicle closing costs', naming a specific verb+resource with clear scope (Canada and US). It distinguishes itself from siblings like get_pricing or get_recalls by detailing jurisdiction coverage and fee categories, making 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.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides extensive context on when to use the tool: when used-vehicle closing costs are needed in Canada or the US, with a detailed breakdown of what is included. It also states prerequisites (EVM_PRIVATE_KEY, live mode) but does not explicitly name alternative tools or exclusions, falling just 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.

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