newegg-pc-builder
Connect to the Newegg PC Builder MCP service to retrieve PC build configurations, component compatibility checks, and build recommendations. Use this skill whenever the user asks about PC builds, custom PC configurations, component compatibility, budget builds, gaming rigs, workstation setups, or anything related to selecting or validating PC components on Newegg — even if they don't mention "PC Builder" by name. Also trigger when the user says things like "help me build a PC", "is this GPU compatible with my motherboard", "what parts do I need for a $1500 gaming build", or "show me Newegg build configs".
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/176wer/uw32w3kdfsdfeNewegg PC Builder MCP Skill
Connects Claude to the Newegg PC Builder MCP service. This skill is fully dynamic: it discovers available tools at runtime and lets the LLM decide which tool to call and how to fill its parameters. No tool names or parameter names are hard-coded, so the skill continues to work even after the MCP server updates its API.
MCP Endpoint: https://apis.newegg.com/ex-mcp/endpoint/pcbuilder
Script: scripts/mcp_client.py
Core Workflow (always follow this order)
Step 1 — Discover tools
Always start by listing available tools. Never assume tool names or parameters from previous runs or documentation.
python scripts/mcp_client.py list_tools
The output contains, for each tool:
name— identifier to use when callingdescription— what it does (may be empty; infer from name + schema)inputSchema.properties— available parameters with types and descriptionsinputSchema.required— mandatory parameters
Step 2 — Select tool and map parameters
Read the list_tools output and decide:
-
Which tool best matches the user's intent?
- Match on description first; fall back to inferring from name + param names
- If multiple tools apply, prefer the most specific one
- If still ambiguous, pick the first one and note the assumption
-
How does user intent map to parameters?
- Only use parameters present in
inputSchema.properties - Free-text params (e.g.
question,query,text): pass the user's request as a natural-language string describing their need - Typed/enum params: map user intent to the closest valid value
- Leave optional params unset unless you have a clear value
- Never invent parameters not present in the schema
- Only use parameters present in
Step 3 — Call the tool
python scripts/mcp_client.py call <tool_name> '<json_arguments>'
Tool name and arguments are determined at runtime from Step 2. Example:
python scripts/mcp_client.py call v2allin '{"question": "gaming PC RTX 5090 9800X3D best price"}'
Windows PowerShell (important)
PowerShell parses outer double quotes before Python runs. Using \"...\" inside "..." often breaks the JSON string (you may see errors like Got: {\ or “invalid JSON”). Prefer one of:
-
Single-quote the whole JSON (no backslash escapes needed):
python scripts/mcp_client.py call v2allin '{"question": "gaming PC RTX 3070"}' -
JSON in a file (most reliable for long or nested payloads):
Set-Content -Path args.json -Encoding utf8 '{"question": "gaming PC RTX 3070"}' python scripts/mcp_client.py call v2allin @args.json
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-176wer-uw32w3kdfsdfe": {
"enabled": true,
"auto_update": true
}
}
}