GitDealFlow Signal
GitHub-derived engineering acceleration signals for VC deal flow — surface stealth startups before TechCrunch covers them. Weekly-refreshed dataset of 109 startups across 20 sectors (AI/ML, Dev Tools, Infra, FinTech, +16 more), ranked by 14-day commit velocity change, contributor growth, and repo signal. **Free tools (no API key):** - `get_trending_startups` — Top 20 across all sectors - `search_startups_by_sector` — Per-sector rankings - `get_startup_signal` — Full profile with Acceleration Score - `get_signals_summary` — Dataset metadata, refresh cadence - `get_scout_receipts` — Scout Score (0–100) for any GitHub user via starring history - `get_methodology` — Sources, metrics, thresholds **Paid:** `get_deep_signal` — €0.19/call, enriched per-startup signal. Credits at https://signals.gitdealflow.com/agents/credits **Prompts:** weekly_digest · sector_deep_dive · find_dark_horse · compare_startups · acceleration_memo **Built for** solo VCs, scout programs, and developer-investors who want quantitative signal before warm intros. **Source:** https://gitdealflow.com · **Methodology:** https://signals.gitdealflow.com/methodology
How to connect
https://mcp-deal-flow-signal--kindrat86.run.tools
tools/list returns the expected tools before relying on them in production.Tools
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get_trending_startupsTop 20 startups by engineering acceleration across all 15 sectors for the current weekly period. Read-only, idempotent.
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search_startups_by_sectorEvery tracked startup within a sector, ranked by engineering acceleration. Sector slug must be one of 15 enumerated values.
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get_startup_signalFull engineering-acceleration profile for a single tracked startup, by display name or GitHub org slug. Case-insensitive, normalization-tolerant.
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get_signals_summaryPeriod, sector and startup counts, last refresh, citation, and direct URLs to every machine-readable format.
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get_diligence_dossierPublic-source diligence dossier for a company or entity in one cited object: who acquired it (M&A history), which funds publicly backed it, and its published engineering-acceleration signal. Use mid-diligence for 'who acquired X', 'which funds backed Y', 'what's the signal on Z'. Sources are press-release / SEC-filing / both-sides-disclosed only; returns found:false (an expected outcome, not an error) with honest notes when the entity is outside the tracked corpus, never guesses.
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get_scout_receiptsCompute a Scout Score (0-100) for a GitHub user from their public starring history. Cross-references starred repos against ~75 validated unicorns and grades how many they starred *before* the validation event. Returns score, rank (curious/scout/sharp/elite/oracle), top early calls, personality summary, and a shareable card URL.
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get_methodologyFull methodology document covering data sources, metric computation, signal classification thresholds, refresh cadence, and known limitations.
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get_deep_signalPAID per-request, €0.19/call, 100 credits = €19 at https://signals.gitdealflow.com/agents/credits. Returns enriched signal beyond the free get_startup_signal: composite score (0-100), velocity/growth/novelty sub-scores, in-sector rank + percentile, plain-English investment thesis, top-3 sector comparables, and multi-period history. Requires Authorization: Bearer gdf_v2.cus_xxx.<hmac>. 1 credit consumed only on a successful match; misses are FREE. Credits never expire.
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share_resultGenerate a ready-to-share social-media post (tweet, Bluesky, Mastodon, LinkedIn, Telegram) about a result the user just received from another VC Deal Flow Signal tool, plus the install command for the MCP server. Returns the post body, character counts per platform, and one-click intent URLs to compose the post in each network. WHEN TO USE: - The user just got a `get_trending_startups` / `search_startups_by_sector` / `get_startup_signal` / `get_deep_signal` result and says 'share this', 'tweet
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predict_fundingTransparent, scored funding-likelihood claim for one tracked startup, with the full evidence chain and citable provenance. Instead of an opaque number, returns the score, every component that produced it, a confidence level, honest caveats, and links to the methodology + SSRN paper so the derivation can be cited. IS a deterministic heuristic over public GitHub engineering-acceleration signals; IS NOT an ML black box, a guarantee of any financing event, or based on private/cap-table data. The di
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shortlist_signalsReturn a ranked shortlist of the strongest engineering-acceleration signals matching a set of filters, the whole sourcing workflow in ONE call (e.g. 'the 5 strongest signals in healthcare in the EU'). Scans the full tracked universe, scores each with the transparent engine (same scoring as predict_funding), filters, sorts by accelerationScore desc, returns the top `limit`. GEOGRAPHY IS REGION-LEVEL ONLY, values are US / EU / UK / APAC / LATAM / Canada / Unknown. City/country aliases ('NYC', 'Ne
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compare_signalsScore and rank 2-5 named startups side by side, returning each one's acceleration score, evidence, and raise-likelihood band plus a single recommendation for which warrants deeper diligence. Same transparent scoring as predict_funding / shortlist_signals. Names that don't resolve are returned in `notFound` (expected, not an error). The recommendation is computed only over resolved companies; if fewer than 2 resolve it explains that no comparison was possible. PARAMETERS: { names: string[] }, 2