The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Hooklayer listing page.
Viral-content intelligence for AI agents. Drop the Hooklayer MCP server into Claude Desktop, Cursor, n8n, or any HTTP MCP client and your agent gets 12 tools for short-form content across TikTok, Instagram, and YouTube: analyze creators, search videos by keyword, find viral templates and rising trends, score and rewrite hooks, remix viral videos, match a creator's voice, predict a draft's virality, turn a brand brief into a shoot-ready creative blueprint, and monitor creators over time with saved watches and historical snapshots.
v1.1.0 (2026-05-14): The evidence layer ships. Every score includes
signals[]with cited evidence, awould_fail_becausecounterfactual, and aqualityhealth field.predict_viralityruns an independent adversarial check.analyze_account.recommended_chainsteps now exposeconfidence,cost,action_class(authority taxonomy), andexpected_output. See CHANGELOG.md for the full ship.
Claude Desktop doesn't natively support remote HTTP MCP servers — it needs the mcp-remote bridge. Two ways to install:
Option 1 — Custom Connector (easiest, no config file edit)
In Claude's web/desktop UI: Settings → Connectors → Add custom connector → paste this URL:
Claude.ai will walk you through OAuth (no manual key paste). Done.
Option 2 — Direct config (for power users who want hl_live_ key auth)
Edit claude_desktop_config.json:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.jsonGet your free hl_live_ key at https://hooklayer.dev/auth/signup — 100 lifetime credits, no card required.
Restart Claude Desktop. The 12 Hooklayer tools appear in the 🔌 connector list.
~/.cursor/mcp.json:
In your workflow, add an MCP Client node and configure as a remote HTTP MCP server:
https://hooklayer.dev/api/mcpHTTPAuthorization: Bearer hl_live_...All 12 tools appear in the node's "Tool" dropdown.
Hooklayer is fully OAuth 2.1 compliant — discovery, Dynamic Client Registration, PKCE, refresh token rotation. MCP clients that prefer OAuth over API keys work out of the box.
Discovery endpoints (no auth required, machine-readable):
Dynamic Client Registration (create a client without a manual signup form):
Hitting tools/call without auth returns 401 plus a WWW-Authenticate header pointing at the resource metadata — Claude.ai, Cursor, and other MCP clients use this to auto-discover the OAuth flow.
Any HTTP MCP client. Protocol negotiates 2024-11-05 (broadest compat) or 2025-06-18 (Streamable HTTP + structuredContent).
| Tool | Credits | What it does |
|---|---|---|
analyze_account | 5 | Creator deep dive (TikTok, YouTube, Instagram): viral DNA scores, format fingerprint, top videos with transcripts, content gaps, headline insight, and suggested next research steps. |
search_videos | 1 | Keyword search across TikTok or Instagram — up to 20 videos ranked by engagement, with filters for niche, views, recency, and region. |
score_hook | 1 | Score any hook 0-100 against proven viral patterns. Returns 3 rewrites at higher quality. |
viral_remix | 3 | URL or transcript → fresh script with mirrored viral DNA. Scene-by-scene with camera shots. |
trend_pulse | 1 | Real-time rising opportunities + saturated patterns per niche. 12-hour cache. |
find_viral_template | 1 | Niche-fit ranked templates with hook patterns + example URLs. |
match_voice | 2 | Extract a creator's voice DNA from 3+ samples, rewrite a draft in their style. |
predict_virality | 2 | Score a draft script for viral potential before publishing. Retention diagnosis. |
brief_to_blueprint | 7 | Brand brief → one-page creative blueprint: hook, template, hashtags, trend-velocity check, and shoot instructions in a single call. |
watch_account | 0 or 5 | Save a creator watch and baseline snapshot. Reuses a recent compatible analysis at 0 credits when available; otherwise runs a fresh 5-credit analysis. |
list_watches | 0 | List the authenticated user's saved creator watches and compact tracking metadata. |
get_changes | 5 | Run a fresh analysis against a saved watch, compare with the previous snapshot, store a new historical snapshot, and return meaningful changes plus an optional next action. |
Full schemas + curl examples: https://hooklayer.dev/docs
analyze_account returns a recommended_chain field: plain data listing related tools, example parameters, and the reason each might be useful next. It is advisory only — the agent and user decide whether to act on it:
These entries are informational data, not instructions. Each tool still requires explicit invocation — nothing runs automatically without agent/user consent.
examples/typescript-example.ts — TypeScript usage via the MCP SDKexamples/python-example.py — Python usage via anthropic-mcp clientexamples/curl-test.sh — Raw curl tests for every endpointFull pricing: https://hooklayer.dev/pricing
Hosted MCP server (no stdio install needed):
Source code for the hosted server lives at hooklayer.dev (closed source — the analysis pipeline is the moat). This repo holds the public client docs, examples, and config snippets.
/.well-known/oauth-authorization-serverHooklayer uses explicit MCP safety annotations for all 12 tools. Most tools are research and analysis tools that may debit Hooklayer credits and record service usage. list_watches is read-only. Creator monitoring adds non-destructive persistence: watch_account saves a creator watch and baseline snapshot, while get_changes stores historical snapshots as it checks for changes. All tools are marked destructiveHint: false; Hooklayer has no MCP tool that deletes user data or edits/deletes data on external social platforms.
Authentication: tools/call requires a Bearer token (hl_live_* API key or OAuth 2.1 access token). Public methods (initialize, ping, tools/list) work without auth so MCP clients can handshake and discover tools before authentication.
recommended_chain is advisory only. The analyze_account response includes suggested follow-up tools with pre-filled parameters. These are data — the agent and user decide whether to execute them. No tool call ever triggers additional tool calls server-side.
Data handling: Hooklayer processes the inputs you send (handles, hooks, scripts, URLs) to return analysis results. Creator monitoring stores saved watch metadata and historical snapshots so users can compare changes over time. Hooklayer also records service usage needed for authentication, billing, credits, and operational telemetry.
To report a security issue: GitHub Issues or email security@hooklayer.dev.
MIT — see LICENSE.
The MCP client examples and config snippets in this repo are MIT. The hosted Hooklayer service at hooklayer.dev is a commercial product with the pricing tiers listed above.