GitDealFlow

Top 20 startups by engineering acceleration across all 20 sectors for the current weekly period. Read-only, idempotent.

Every tracked startup within a sector, ranked by engineering acceleration. Sector slug must be one of 20 enumerated values.

Full engineering-acceleration profile for a single tracked startup, by display name or GitHub org slug. Case-insensitive, normalization-tolerant.

Period, sector and startup counts, last refresh, citation, and direct URLs to every machine-readable format.

Public-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-r

Compute 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 summa

Full methodology document covering data sources, metric computation, signal classification thresholds, refresh cadence, and known limitations.

PAID 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 comp

Generate 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 comp

Transparent, 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

Return 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 fintech in the EU'). Scans the full tracked universe, scores each with the transparent engine (same scoring as predict_fun

Score 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 `n