Google Search Console integration for Model Context Protocol (MCP)
Creator Research is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
What it does
MCP server that turns videos, channels, articles, and PDFs into structured knowledge
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user creator-research-mcp '--' npx -y creator-research-mcp
# 2 — ask your agent for something these tools do
# get_channel_about, get_channel_monetization, get_comments and 20 more
Creator Research costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Creator Research as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user creator-research-mcp '--' npx -y creator-research-mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
23 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Everything from a channel's About page in one call — name, description, subscriber/view/video
No parameter schema on file.
Best-effort, deterministic detection of non-AdSense monetization (digital products, courses,
No parameter schema on file.
Fetches the most relevant public comments on a video/post via yt-dlp (YouTube or Instagram; no API key)
No parameter schema on file.
Compares 2-10 existing analyses: what technologies/concepts/syllabus they share and what's unique to each
No parameter schema on file.
Fetches public YouTube or Instagram comments (same source as get_comments) and groups the ones asking
No parameter schema on file.
Fetches a channel/profile sample and computes deterministic statistics: median views/duration,
No parameter schema on file.
Compares 2-10 channels/profiles side by side: median views, typical duration, publish
No parameter schema on file.
Assembles a course from N analyses: deduplicates topics repeated across videos, preserves teaching
No parameter schema on file.
Builds a roadmap from accumulated knowledge: technologies and concepts from the corpus ordered
No parameter schema on file.
Returns an existing analysis by analysisId or by url (finds the latest done analysis for that URL).
No parameter schema on file.
Returns the segments of the video the audience rewinds/replays the most (the 'most replayed' that YouTube
No parameter schema on file.
Records a manual measurement of a profile (Instagram, TikTok, or any platform without automatic
No parameter schema on file.
Returns the views/likes/comments snapshots saved for this URL (via list_videos or
No parameter schema on file.
Joins a YouTube video's replay heatmap (get_video_heatmap) with its transcript (get_transcript) by
No parameter schema on file.
Persists an analysis you (the client LLM) made of a transcript obtained via get_transcript.
No parameter schema on file.
Searches ALL facets of ALL saved analyses. Answers questions like
No parameter schema on file.
Lists recent analyses (title, source, status, AI engine used).
No parameter schema on file.
Extracts metadata, engagement (views/likes/comments), author details, thumbnails, carousel items, and text from a source (video with subtitles,
No parameter schema on file.
What's working RIGHT NOW on YouTube, beyond a specific channel — useful for content ideas
No parameter schema on file.
Lists the videos of a channel/profile (YouTube, TikTok) with views, duration, and outlier
No parameter schema on file.
Searches YouTube for a topic, then for each result computes outlierScore = the video's view
No parameter schema on file.
Keyword search across all of YouTube (not limited to one channel) — use this to find
No parameter schema on file.
No parameter schema on file.
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Creator Research or to call it, at any volume.
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | moderate | capability blast radius (moderate) — client exposure if the model is manipulated | −3 |
| verification-discount | repo | publisher verification (public source) — no provenance, but the source is public and inspectable | −1 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tools were statically extracted from the published source (23 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. moderate is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| capabilities | no tags | |
| get_channel_about | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| get_channel_monetization | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| get_comments | untrusted-input | untrusted-input: keyword "fetch", parameter "url" with "fetch" in the description |
| compare | no tags | |
| get_content_ideas | untrusted-input | untrusted-input: keyword "fetch", parameter "url" with "fetch" in the description |
| analyze_creator | no tags | |
| compare_creators | no tags | |
| generate_course | no tags | |
| generate_roadmap | no tags | |
| get_analysis | no tags | |
| get_video_heatmap | no tags | |
| import_profile_snapshot | no tags | |
| get_metrics_history | no tags | |
| get_retention_moments | no tags | |
| save_analysis | no tags | |
| search_knowledge | no tags | |
| history | no tags | |
| get_transcript | no tags | |
| get_trending_videos | no tags | |
| list_videos | no tags | |
| search_viral_videos | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| search_youtube_videos | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 0.5.0. Pin a release below and the command asks for exactly that one.
No release note was published with this version.
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Any signed-in account that does not publish Creator Research can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.