Supplies standardized wheel fitment specifications for Shopify e-commerce storefronts, facilitating accurate product data integration.
Supplies standardized wheel fitment specifications for WooCommerce e-commerce storefronts, facilitating accurate product data integration.
Official Model Context Protocol (MCP) Server for BoltPatternHQ — providing AI coding assistants (Claude, Cursor, Windsurf, VS Code) with instant, zero-latency automotive wheel fitment, PCD (Pitch Circle Diameter), center bore, thread size, and offset reference data.
🌐 Official Website & Live Search Engine: https://boltpatternhq.com/
📚 MCP & API Documentation: https://boltpatternhq.com/data/
⚡ Get Full 11,601-Row B2B Dataset ($29 Lifetime License): https://boltpatternhq.com/data/
👨💻 Developer Profile & Blog: https://double2.hatenablog.com/
Related MCP server: PostIdentity MCP Server
When developing automotive e-commerce storefronts (Shopify / WooCommerce), wheel fitment calculators, CAD parts models, or ERP systems, LLMs often suffer from hallucinated vehicle wheel specifications.
The BoltPatternHQ MCP Server bridges your AI programming environment directly to standardized, OEM-verified automotive engineering specifications:
0ms Offline Querying: Built-in curated sample library of iconic JDM, Euro, and American vehicles (Mazda MX-5 Miata, Suzuki Jimny, Toyota Raize/Rocky, BMW 3 Series, VW Golf GTI, Ford Mustang, Tesla Model 3/Y, etc.).
Standardized Engineering Metrics: PCD (e.g., 4x100, 5x114.3, 5x139.7), Center Bore (in mm), Offset Range (ET in mm), Lug Nut Thread Sizes (M12x1.5, M14x1.25, Ball/Spherical seat notes), and lug bolt counts.
Seamless B2B Integration: Easily prototype your automotive scripts with our sample dataset, then scale seamlessly to our complete 11,601-row master CSV database.
This server exposes 3 standardized AI tools via stdio:
search_bolt_patternSearch vehicle wheel specs by manufacturer make, model name, chassis code, year, or exact PCD string.
Input: { "query": "Jimny" } or { "query": "5x114.3" } or { "query": "Roadster" }
Output: Detailed fitment report including center bore, offset tolerance, and OEM hub notes.
get_make_modelsList all supported automotive brand manufacturers and highlighted models available in the public MCP library.
Input: {}
Output: Categorized markdown overview of makes and vehicle generations.
get_sample_dataExport the full curated JSON sample dataset for direct ingestion into CAD workflows, SQL databases, or unit tests.
Input: {}
Output: Standardized JSON array of vehicle fitment objects.
You can run the server instantly without installing any Python virtual environments using Node.js and npx.
Add the following configuration to your Claude Desktop config file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"boltpatternhq": {
"command": "npx",
"args": ["-y", "boltpatternhq-mcp"]
}
}
}In Cursor, open Settings -> Features -> MCP Servers -> Add new MCP server:
Name: boltpatternhq
Type: command
Command: npx -y boltpatternhq-mcp
To run or install via Smithery:
npx -y @smithery/cli install @double2dev/boltpatternhq-mcp --client claudeAdd to your workspace or global MCP settings JSON:
{
"mcpServers": {
"boltpatternhq": {
"command": "npx",
"args": ["-y", "boltpatternhq-mcp"]
}
}
}If you prefer to clone and modify the source code:
# Clone repository
git clone https://github.com/double2dev/boltpatternhq-mcp.git
cd boltpatternhq-mcp
# Install dependencies
npm install
# Compile TypeScript
npm run build
# Test server stdio launch
npm startThis MCP server provides instant access to the BoltPatternHQ Public Sample Library for prototyping and AI debugging.
For production e-commerce websites, global tire/wheel export operations, and commercial parts-lookup systems, unlock the complete master database:
11,601 Verified Model-Year Records (1992–2026)
66 Global Automotive Manufacturers (JDM, USDM, Euro, ADM, EV platforms)
Standardized UTF-8 CSV & JSON Formats ready for instant SQL/ERP import
One-Time Buy-Once Lifetime License ($29 USD) — no recurring monthly MRR API fees!
👉 Download Master Database Here: https://boltpatternhq.com/
This project is structured and verified for inclusion in the following global AI ecosystem registries:
Official MCP Registry (.mcp/server.json)
This MCP server wrapper and public sample data are open-sourced under the MIT License. Built with ❤️ by double2 @個人開発者.
List all automotive manufacturers and supported model highlights available in the BoltPatternHQ sample library.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states what is listed but lacks details on response format, volume, or any constraints beyond what is obvious.
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?
Single sentence that is front-loaded with the action and resource, no redundant information.
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 tool with no parameters and no output schema, the description provides sufficient context (scope, library) to understand its role.
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?
There are no parameters, and schema coverage is 100%. The description adds meaning by specifying the content of the list, achieving the baseline of 4 for zero-parameter tools.
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 verb 'list' and the resource 'automotive manufacturers and supported model highlights', and distinguishes from sibling tools like search_bolt_pattern and get_sample_data.
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 implied as a preliminary step to get available makes/models, but there is no explicit guidance on when to use this tool versus the alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export the complete curated 50-row JSON sample dataset for CAD integration, wheel fitment calculators, and B2B testing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool exports a complete, curated dataset of exactly 50 rows, which is a read‑only operation. Without annotations, this is clear, but it does not mention potential side effects or authentication requirements. However, given the trivial nature of the tool (no destructive actions), it is sufficiently 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?
The description is a single, well‑structured sentence that front‑loads the core action ('Export the complete curated 50‑row JSON sample dataset') and appends specific use cases. Every word contributes to clarity with no 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?
Given the tool's simplicity (no parameters, no output schema), the description provides sufficient context about the output and its purpose. It could optionally mention that the data is read‑only or provide a link to documentation, but the current text adequately supports 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?
There are no parameters; the schema is empty. The description adds meaning by specifying the dataset's size (50 rows), format (JSON), and intended use cases, which goes beyond the schema alone. According to the rule, zero parameters warrant a baseline of 4, and the description meets this.
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 exports a curated 50-row JSON sample dataset for specific integration and testing purposes. It distinguishes itself from sibling tools (search_bolt_pattern and get_make_models) which perform different tasks like searching or retrieving models.
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 CAD integration, wheel fitment calculators, and B2B testing but provides no explicit guidance on when to use this tool versus the alternatives. With sibling tools listed, a note explaining that this exports sample data while siblings handle patterns or models would improve the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search automotive wheel fitment specifications (PCD / bolt pattern, center bore, offset ET, thread size, lug count) by vehicle make, model, year, or exact PCD (e.g. 'Jimny', 'Roadster', '5x114.3', 'Raize', 'BMW 3 Series').
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search term: vehicle model name, manufacturer make, chassis code, or bolt pattern (e.g., 'Jimny', 'MX-5', '4x100', 'Tesla'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must convey behavioral traits. It states it searches and gives examples, but does not mention pagination, response format, query limits, or behavior for invalid inputs. Given it's a read-only search tool, essential traits are partially covered but incomplete.
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?
Single sentence with examples, no wasted words. Efficiently conveys tool purpose and input format.
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 one required parameter and no output schema, the description covers the input but lacks details on output structure, expected result count, or error handling. For a simple search tool, it meets basic needs but could be more 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 describes the single 'query' parameter with examples. Description adds context about what is searched (wheel fitment specs), but no additional parameter-specific meaning beyond the schema. Schema coverage is 100%, so baseline 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?
Clear verb 'Search' with specific resource 'automotive wheel fitment specifications'. Lists the types of specifications (PCD, center bore, etc.) and search methods (by make, model, year, exact PCD). Distinct from siblings: get_make_models likely returns lists of models, and get_sample_data provides sample data, so this tool focuses on fitment search.
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?
No explicit guidance on when to use this tool versus alternatives like get_make_models. The description implies usage for searching fitments by vehicle details or patterns, but does not mention prerequisites or when this tool is preferred over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get_make_modelsget_sample_datasearch_bolt_patternEach tool has a clearly distinct purpose: searching fitments, listing manufacturers/models, and exporting sample data. No overlap in functionality.
All tool names follow a consistent verb_noun pattern using snake_case (search_bolt_pattern, get_make_models, get_sample_data), making them predictable and easy to understand.
Three tools is reasonable for a focused automotive fitment data server. While the set is small, each tool serves a distinct need without feeling sparse.
The tool set covers the core operations: search, list models, and export data. It misses update/delete operations, but these are likely unnecessary for a read-only database server.
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