competitive_deep_dive
ActiveTool of gapup-mcp
Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative). Pair tool: `competitor_intel` for LLM-narrated board briefing + slide script. Aggregates Wikipedia, Yahoo Finance, SEC EDGAR, Wayback Machine, DuckDuckGo, HackerNews, domain scraping — all keyless. Returns agent-shaped JSON: KPIs (funding, employees, revenue, market cap), P0/P1/P2 competitive signals, pricing radar, competitor comparison matrix, Wayback timeline, positioning (sector/industry/icp_hypothesis/moat_signals), quality score. Every field is sourced or marked unavailable — no hallucinated figures. SLA: p50 ~25s, p95 ~30s · score 80+ on listed targets (US/EU/foreign) · score ~40 on private companies (no EDGAR/Yahoo data). Use sync for batch agents (≤30s tolerance). Use `competitive_deep_dive_async` + `competitive_deep_dive_result(job_id)` for conversational agents. Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard).
Parameters schema
{
"type": "object",
"required": [
"company"
],
"properties": {
"async": {
"type": "boolean",
"description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts."
},
"depth": {
"enum": [
"easy",
"medium",
"hard"
],
"type": "string",
"description": "Research depth: 'easy' = Wikipedia + DDG (fast, ~15s); 'medium' = + Yahoo Finance + EDGAR + Wayback (default, ~45s); 'hard' = + HackerNews + domain surfaces + competitor deep dive (~120s)"
},
"company": {
"type": "string",
"maxLength": 200,
"minLength": 2,
"description": "Name or domain of the target company (e.g. 'Salesforce', 'notion.so', 'HubSpot CRM')"
},
"competitors": {
"type": "array",
"items": {
"type": "string",
"maxLength": 200
},
"maxItems": 5,
"description": "Optional list of competitor names or domains to include in the comparison matrix (max 5)"
}
}
}Parent server
gapup-mcp
https://github.com/getgapup/gapup-mcp-public
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