Server data from the Official MCP Registry
161 verified dark data sources across 25+ domains with semantic routing and API aggregation
161 verified dark data sources across 25+ domains with semantic routing and API aggregation
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. ⚠️ Package registry links to a different repository than scanned source. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
3 files analyzed · 1 issue found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-parnasheps-eng-dark-search-protocol": {
"args": [
"-y",
"dark-search-mcp-server"
],
"command": "npx"
}
}
}From the project's GitHub README.
MCP Server for the Dark Search Protocol — searches deep and dark data sources not indexed by mainstream search engines.
Mainstream search engines (Google, Bing) only index the Surface layer. But the most valuable technical data lives in:
DSP gives AI agents access to these hidden layers with confidence-scored, cross-verified results.
npx @dark-search/mcp-server
Or add to your MCP client config:
{
"mcpServers": {
"dark-search": {
"command": "npx",
"args": ["-y", "@dark-search/mcp-server"]
}
}
}
dark_searchSearch deep and dark data sources. AI automatically uses this when web search returns insufficient results.
Parameters:
query (string, required) — Search query. Supports English and Chinese.max_results (number, optional) — Max results to return (default: 10)min_confidence (number, optional) — Minimum confidence: 0.5=high, 0.3=moderate, 0.15=exploratory (default: 0.15)layers (array, optional) — Filter by layer: ["surface", "deep", "dark"]Returns:
{
"query": "refrigerant leak diagnostic",
"domain": "hvac",
"total_sources_searched": 2,
"results": [
{
"source_name": "Refrigerant Leak Pattern Database",
"layer_name": "Dark",
"confidence": 0.95,
"confidence_level": "HIGH",
"cross_verified": true,
"why_this_source": "Dark layer — not indexed by mainstream search; Highly relevant with strong tag matches"
}
],
"meta": {
"pipeline": "6-step: Parse→Route→Retrieve→Verify→Rank→Correct",
"confidence_formula": "layerBase × (0.3 + 0.7 × relevance) + (cross_verified ? 0.1 : 0)",
"thresholds": {
"HIGH (>= 0.5)": "Directly citeable in AI responses",
"MID (0.3-0.49)": "Use with attribution, needs human confirmation",
"LOW (0.15-0.29)": "Exploratory only, do not cite directly"
}
}
}
list_sourcesList available dark data sources. Filter by domain or layer.
add_sourceSubmit a new data source to the registry. Supports bilingual tags for multilingual search.
| Level | Range | AI Behavior |
|---|---|---|
| HIGH | >= 0.5 | Directly cite in AI responses |
| MID | 0.3-0.49 | Cite with attribution, flag for confirmation |
| LOW | 0.15-0.29 | Exploratory only, do not cite |
| NOISE | < 0.15 | Filtered out |
Formula: confidence = layerBase × (0.3 + 0.7 × relevance) + (cross_verified ? 0.1 : 0)
Layer weights: Surface=0.3, Deep=0.6, Dark=0.9
Semantic mapping bridges Chinese queries to English-tagged sources:
MIT
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