pyr0ball
c93466c037
Three tools: snipe_search (GPU-scored trust-ranked), snipe_enrich (deep BTF scraping), snipe_save (persist search to Snipe UI). GPU inference scoring uses VRAM + arch tier weighted composite. LLM-condensed output trims verbose listing dicts to trust/price/GPU/url. Configure via ~/.claude.json with SNIPE_API_URL env var pointing at local or cloud API.
262 lines
10 KiB
Python
262 lines
10 KiB
Python
"""Snipe MCP Server — eBay search with trust scoring and GPU inference-value ranking.
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Exposes three tools to Claude:
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snipe_search — search eBay via Snipe, GPU-scored and trust-ranked
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snipe_enrich — deep seller/listing enrichment for a specific result
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snipe_save — persist a productive search for ongoing monitoring
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Run with:
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python -m app.mcp.server
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(from /Library/Development/CircuitForge/snipe with cf conda env active)
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Configure in Claude Code ~/.claude.json:
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"snipe": {
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"command": "/devl/miniconda3/envs/cf/bin/python",
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"args": ["-m", "app.mcp.server"],
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"cwd": "/Library/Development/CircuitForge/snipe",
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"env": { "SNIPE_API_URL": "http://localhost:8510" }
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}
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"""
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from __future__ import annotations
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import asyncio
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import json
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import os
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import httpx
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from mcp.server import Server
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from mcp.server.stdio import stdio_server
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from mcp.types import TextContent, Tool
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_SNIPE_API = os.environ.get("SNIPE_API_URL", "http://localhost:8510")
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_TIMEOUT = 120.0
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server = Server("snipe")
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@server.list_tools()
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async def list_tools() -> list[Tool]:
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return [
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Tool(
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name="snipe_search",
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description=(
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"Search eBay listings via Snipe. Returns results condensed for LLM reasoning, "
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"sorted by composite value: trust_score × gpu_inference_score / price. "
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"GPU inference_score weights VRAM and architecture tier — tune with vram_weight/arch_weight. "
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"Use must_include_mode='groups' with pipe-separated OR alternatives for broad GPU coverage "
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"(e.g. 'rtx 3060|rtx 3070|rtx 3080'). "
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"Laptop Motherboard category ID: 177946."
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),
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inputSchema={
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"type": "object",
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"required": ["query"],
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"properties": {
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"query": {
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"type": "string",
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"description": "Base eBay search keywords, e.g. 'laptop motherboard'",
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},
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"must_include": {
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"type": "string",
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"description": (
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"Comma-separated AND groups; use | for OR within a group. "
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"E.g. 'rtx 3060|rtx 3070|rx 6700m, 8gb|12gb|16gb'"
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),
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},
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"must_include_mode": {
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"type": "string",
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"enum": ["all", "any", "groups"],
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"default": "groups",
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"description": "groups: pipe=OR comma=AND. Recommended for multi-GPU searches.",
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},
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"must_exclude": {
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"type": "string",
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"description": (
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"Comma-separated terms to exclude. "
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"Suggested: 'broken,cracked,no post,for parts,parts only,untested,"
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"lcd,screen,chassis,housing,bios locked'"
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),
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},
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"max_price": {
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"type": "number",
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"default": 0,
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"description": "Max price USD (0 = no limit)",
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},
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"min_price": {
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"type": "number",
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"default": 0,
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"description": "Min price USD (0 = no limit)",
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},
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"pages": {
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"type": "integer",
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"default": 2,
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"description": "Pages of eBay results to fetch (1 page ≈ 50 listings)",
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},
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"category_id": {
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"type": "string",
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"default": "",
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"description": (
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"eBay category ID. "
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"177946 = Laptop Motherboards & System Boards. "
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"27386 = Graphics Cards (PCIe, for price comparison). "
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"Leave empty to search all categories."
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),
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},
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"vram_weight": {
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"type": "number",
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"default": 0.6,
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"description": (
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"0–1. Weight of VRAM in GPU inference score. "
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"Higher = VRAM is primary ranking factor. "
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"Use 1.0 to rank purely by VRAM (ignores arch generation)."
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),
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},
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"arch_weight": {
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"type": "number",
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"default": 0.4,
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"description": (
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"0–1. Weight of architecture generation in GPU inference score. "
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"Higher = prefer newer GPU arch (Ada > Ampere > Turing etc.). "
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"Use 0.0 to ignore arch and rank purely by VRAM."
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),
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},
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"top_n": {
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"type": "integer",
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"default": 20,
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"description": "Max results to return after sorting",
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},
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},
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},
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),
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Tool(
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name="snipe_enrich",
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description=(
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"Deep-dive enrichment for a specific seller + listing. "
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"Runs BTF scraping and category history to fill partial trust scores (~20s). "
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"Use when snipe_search returns trust_partial=true on a promising listing."
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),
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inputSchema={
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"type": "object",
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"required": ["seller_id", "listing_id"],
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"properties": {
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"seller_id": {
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"type": "string",
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"description": "eBay seller platform ID (from snipe_search result seller_id field)",
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},
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"listing_id": {
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"type": "string",
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"description": "eBay listing platform ID (from snipe_search result id field)",
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},
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"query": {
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"type": "string",
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"default": "",
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"description": "Original search query — provides market comp context for re-scoring",
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},
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},
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},
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),
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Tool(
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name="snipe_save",
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description="Persist a productive search for ongoing monitoring in the Snipe UI.",
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inputSchema={
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"type": "object",
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"required": ["name", "query"],
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"properties": {
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"name": {
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"type": "string",
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"description": "Human-readable label, e.g. 'RTX 3070+ laptop boards under $250'",
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},
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"query": {
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"type": "string",
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"description": "The eBay search query string",
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},
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"filters_json": {
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"type": "string",
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"default": "{}",
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"description": "JSON string of filter params to preserve (max_price, must_include, etc.)",
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},
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},
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},
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),
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]
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@server.call_tool()
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async def call_tool(name: str, arguments: dict) -> list[TextContent]:
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if name == "snipe_search":
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return await _search(arguments)
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if name == "snipe_enrich":
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return await _enrich(arguments)
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if name == "snipe_save":
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return await _save(arguments)
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return [TextContent(type="text", text=f"Unknown tool: {name}")]
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async def _search(args: dict) -> list[TextContent]:
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from app.mcp.formatters import format_results
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# Build params — omit empty strings and zero numerics (except q)
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raw = {
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"q": args.get("query", ""),
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"must_include": args.get("must_include", ""),
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"must_include_mode": args.get("must_include_mode", "groups"),
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"must_exclude": args.get("must_exclude", ""),
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"max_price": args.get("max_price", 0),
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"min_price": args.get("min_price", 0),
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"pages": args.get("pages", 2),
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"category_id": args.get("category_id", ""),
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}
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params = {k: v for k, v in raw.items() if v != "" and v != 0 or k == "q"}
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async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
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resp = await client.get(f"{_SNIPE_API}/api/search", params=params)
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resp.raise_for_status()
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formatted = format_results(
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resp.json(),
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vram_weight=float(args.get("vram_weight", 0.6)),
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arch_weight=float(args.get("arch_weight", 0.4)),
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top_n=int(args.get("top_n", 20)),
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)
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return [TextContent(type="text", text=json.dumps(formatted, indent=2))]
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async def _enrich(args: dict) -> list[TextContent]:
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async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
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resp = await client.post(
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f"{_SNIPE_API}/api/enrich",
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params={
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"seller": args["seller_id"],
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"listing_id": args["listing_id"],
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"query": args.get("query", ""),
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},
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)
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resp.raise_for_status()
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return [TextContent(type="text", text=json.dumps(resp.json(), indent=2))]
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async def _save(args: dict) -> list[TextContent]:
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async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
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resp = await client.post(
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f"{_SNIPE_API}/api/saved-searches",
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json={
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"name": args["name"],
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"query": args["query"],
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"filters_json": args.get("filters_json", "{}"),
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},
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)
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resp.raise_for_status()
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data = resp.json()
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return [TextContent(type="text", text=f"Saved (id={data.get('id')}): {args['name']}")]
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async def _main() -> None:
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async with stdio_server() as (read_stream, write_stream):
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await server.run(
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read_stream,
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write_stream,
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server.create_initialization_options(),
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)
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if __name__ == "__main__":
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asyncio.run(_main())
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