@nooticr/mcp

MCP (Model Context Protocol) server for nooticr.

Gives an AI assistant three things: it can read real social posts across ten networks (TikTok, Instagram, YouTube, X, Reddit, LinkedIn, Douyin, Xiaohongshu, Weibo, Bilibili), understand them — transcript, video frames, comments, the numbers — and make something from what it learned: hooks, variants to film, a scored draft, a repurposed thread.

The understanding is your model's, not ours. Every tool here fetches material and hands it over with an account of what to do with it; none of them ask a model of ours for an opinion first. You pay for the fetch and nothing else.

It also monitors a name: search_mentions sweeps nine of those networks for every comment that says your brand, inside a date window you choose, and search_spoken_mentions reads the words actually said out loud in TikTok, YouTube and Douyin videos for the mentions that were never typed anywhere.

Runs over stdio locally or as a hosted connector at https://mcp.nooticr.com/mcp. Billed against your nooticr credits; new accounts get 20 free.

Install (one link)

Claude Code — register the marketplace, then install the plugin:

/plugin marketplace add Nooticr/nooticr-mcp
/plugin install nooticr@nooticr

Claude Code / CLI without the plugin:

claude mcp add nooticr --user -- npx -y @nooticr/mcp
npx @nooticr/mcp login   # one-time sign-in (Google)

Cursor / any stdio MCP client (claude_desktop_config.json, .mcp.json, …):

{
  "mcpServers": {
    "nooticr": {
      "command": "npx",
      "args": ["-y", "@nooticr/mcp"],
      "env": { "NOOTICR_BASE_URL": "https://api.nooticr.com" }
    }
  }
}

Tools

90 tools, grouped by what you are trying to do. Prices are in nooticr credits and match what the server actually charges.

The job tools under Answer a question you actually have are not endpoint wrappers. Each names a job, fans out over the calls that job needs, groups the evidence by whatever you are deciding about, gives every item an id a follow-up tool can act on, and hands the reading to your model rather than to ours. They fan out, so they cost the sum of what they fetched and every one of them caps that fan-out with an argument.

Read a post

Tool	Credits	What it is for
get_social_media	1	The post's facts and media — contentType, title, caption, author, stats, direct media URLs, plus an inline thumbnail. Use when you want the post itself and nothing interpreted.
get_post_transcript	1	The words actually spoken. Where the platform publishes a caption track (TikTok, Douyin, YouTube) it is read as-is — exact rather than inferred, and far cheaper than watching the video; everywhere else the post's own audio is transcribed, which needs speech-to-text configured on the server. format: "srt" or "vtt" also returns a ready caption file built from the track's own timing, at no extra cost. Use before any analysis when the wording matters.
get_post_frames	2	Frames sampled evenly across a post's video, returned as images you can actually look at — not a description of them. ffmpeg opens the stream directly rather than downloading it, so HLS works and an expired link is re-resolved on the spot. Verified live at 3/3 on TikTok, YouTube, Instagram, Douyin and X; Reddit works on video posts. A carousel or slideshow returns its own images unchanged. Each frame costs roughly 1,200 tokens of your context.
get_post_comments	2	Top comments plus the themes the platform clusters them into, with which ones the creator pinned or liked. Use when you want to read what people wrote.

Understand a post

Tool	Credits	What it is for
analyze_post_fast	2	The post's transcript, caption and stats — everything but the pictures, which is what makes it the cheap read. Two fetches: get_social_media (1) and get_post_transcript (1). The sensible default.
analyze_post	3	Frames sampled across the video, as images your model can actually look at, plus the transcript. Two fetches: get_post_frames (2) and get_post_transcript (1). Use when the visuals are the point — framing, editing, on-screen text.
understand_social_post	3	The same two fetches, asked for a description of what physically happens on screen rather than why it works. Use when you need the events, not the strategy.
summarize_post_comments	2	The comment section, every comment with a stable id, and the taxonomy to label them with — sentiment, and whether each is praise, a complaint, a bug report, a question, a request, a comparison or spam. The same get_post_comments call, at the same price as reading them directly. Pass verbatim: true to have every item rendered whole for quoting rather than clipped to fit; same price.
show_comment_review	free	Draws the classifications your model produced — every comment with its sentiment and category, filterable and selectable. Makes no requests; it only renders what you pass it.
show_post_analysis	free	Draws the analysis your model wrote from analyze_post/analyze_post_fast/understand_social_post — hook strength, script structure, quotable lines, suggested hashtags and more, whichever fields you produced. Makes no requests; it only renders what you pass it.
compare_social_posts	1	The first of two to five posts, fetched with its stats, and the comparison left to you. Fetch the rest with get_social_media at 1 credit each. Use when performance differs and you need to know why.
show_compared_posts	free	Draws the comparison your model wrote from compare_social_posts — each post scored, the winner marked, what differed and the next test worth running. Makes no requests; it only renders what you pass it.

Research a niche or a creator

Tool	Credits	What it is for
discover_social_posts	2	Recent posts for a niche across nine networks, with inline thumbnails and limit/offset pagination. Use to find posts to look at.
get_user_posts	2	One creator's recent posts with stats. Use to scan an account.
search_creators	2	People by what they make — designers, developers, photographers, anyone with an audience — found by craft, niche or keyword. Use when you know the kind of person but not their names. Searches TikTok, Instagram and Xiaohongshu; not YouTube, Douyin, X, Reddit or LinkedIn, which cannot be creator-searched upstream.
get_similar_creators	2	Lookalikes for a creator that already works.
suggest_creator_identity	2 per network searched, plus 2 to find the handle (6 by default)	The same person on the other networks, from one handle you already know — with the evidence for each: a link published in both bios, one bio naming the other account, an identical handle, a matching display name. Suggests, never merges: nothing is stored, a human confirms, and two accounts' follower counts are never added together. TikTok, Instagram and Xiaohongshu.
discover_sounds	2	Trending audio with playable previews. Sound is a major ranking signal on TikTok.
discover_hashtags	2	What to tag with. TikTok comes from the Creative Center trend board — volumes plus rising/cooling/steady. The other eight searchable networks have no trend board upstream, so tags are counted across a live sweep of a niche you name: real and current, but a single sample, so no rising/cooling signal. source says which you got. LinkedIn cannot be swept.
find_hook_pattern	2	A creator's recent posts, so their opening lines can be read as a set and turned into fill-in-the-blank templates. One get_user_posts call.
search_mentions	2 per network (5 for Xiaohongshu)	Brand monitoring. Every comment that names a term, across nine networks at once, grouped under the post it was left on. A brand is named far more often in the replies than in a caption, so the comment is the unit — not the post. Takes a since date to read a past window, and pages with offset/pageSize so a nine-network sweep does not arrive all at once. Does not read speech inside a video — search_spoken_mentions does, on TikTok, YouTube and Douyin.
watch_creator	free	Add a creator to your watchlist. Stores the handle only — nothing is fetched.
unwatch_creator	free	Drop a creator from the watchlist.
list_watchlist	free	Who you are watching: handle, platform, note, and when you last caught up on each.
catch_up_watchlist	2 per creator	What everyone you watch has posted since your last catch-up. Compares against the snapshot taken last time and moves it forward, so it answers "what is new" rather than "what exists".
create_brand_watch	free to call	Schedule a recurring sweep and get emailed only what is new. kind: "mentions" (default) is a recurring search_mentions sweep for term/platforms; kind: "competitor" is a recurring get_user_posts check on one creator (handle/platform), mailing only what beats their own recent median; kind: "report" runs job: {tool, args} — niche_report (mailed as written every run) or discover_social_posts (only unseen posts) — on the schedule; kind: "portfolio" sweeps up to 25 terms (your brand, products, competitors) across the same networks under one budget and mails one digest with each term's share of voice. Two calls by design: the first returns the quote (cost per run, cadence, cost per day) and a confirmationToken; nothing is created or charged until you call it again with confirm: true and that token. Each run then bills the same as calling the matching tool yourself — 2 credits per network (5 for Xiaohongshu) for mentions, a flat 2 credits for competitor, the job tool's own price for a report (3 for niche_report), and a mentions sweep per term for a portfolio — whose budget is split evenly and trims networks, never terms, and which refunds any term whose sweep fails.
list_brand_watches	free	Every watch you have scheduled: term, networks, cadence, cost per run, credits spent so far, runs made, and when the next one is due.
mention_trend	free	What a watch has seen over time, which nothing else here answers — every other read is about now. One point per run: when it ran, how many mentions it found, how many were new, and both counts per network, so "which network is growing" and "did it go quiet or did we stop looking" come apart. On a competitor watch each point carries the median that run measured, which turns "they beat their own median" into "and that median has doubled". Plus the mentions more than one run has seen. Free because the sweeps were billed when they ran; charging again to read what was already paid for is the thing it fixes.
update_watch_portfolio	free	Add or remove a portfolio watch's terms by watchId. No new confirmation, because the per-run budget you agreed to does not move — more terms split it more ways, and the reply names any network a run no longer reaches.
stop_brand_watch	free	Stop a watch by watchId or term. Immediate — the next run does not happen and nothing more is charged.
niche_report	2	Recent posts in a niche with their stats, so the dominant formats, hook patterns and the gaps nobody fills can be read off them. One discover_social_posts call. Use when deciding what to make.
analyze_creator_profile	2	A creator's recent posts with their stats — the material of a teardown: niche, themes, hook formula, what over- and underperforms, who the audience is. One get_user_posts call.

Answer a question you actually have

Tool	Credits	What it is for
answer_my_audience	2 + 2 per post opened (14 by default)	The mirror of search_mentions. The questions waiting under your own posts: recent posts fetched, comments read on each, grouped under the post they were left on, every comment with a stable id, and the ones that read like questions or requests flagged and sorted to the top. It finds and drafts — it cannot post a reply: nooticr does not post comments on any network, even where a connection's grant allows managing them. limit caps how many posts are opened, which is the price.
prepare_handoff	free	The hand-off to your tracker. Turns items you classified — a bug report in a comment, a complaint said out loud in a video — into the exact title, body and labels to file through whichever GitHub, Jira or Linear MCP server the host also has connected. nooticr files nothing and holds no tracker credential. The body carries the quote fenced and framed as a third-party report rather than as instructions, because the issue is read next by a coding agent; contact details are redacted, @handles and #numbers are defanged so filing notifies nobody by accident, and a searchFirst string dedupes a second sweep against the first. Makes no requests.
show_audience_replies	free	Lays your drafts out for a person to work through, grouped under the post, each with what you decided to do about it. Sends nothing; fetches nothing.
show_standings	free	Draws the standings your model read out of compare_creators or watchlist_standings — one row per creator, with the window and the median it was scored against. ranking records which axis you ordered on, and tooThin draws a creator with too short a window as unranked rather than last: missing from a comparison is not the same as losing it. Makes no requests.
show_trend	free	Draws the trend your model read out of mention_trend. tooShort marks a series with too few points to call a direction, and edgeIsRecordStart marks a left edge that is where the record begins rather than where the conversation did — a chart hiding either is the mistake this view could otherwise make on your behalf. Makes no requests.
track_creator	2	What a creator shipped, and which of it beat their own median rather than a raw view count that mostly measures follower count. One post list, whatever the window. If they are on your watchlist it also marks what is new since your last check and moves that marker forward — its own marker, not the one catch_up_watchlist keeps.
compare_creators	2 per creator (4-10)	Two to five creators side by side on the one axis that compares: each post's ratio to that creator's own median. Per creator you get the window that was scored, their median, the share of it that beat that median, how hard they beat it when they did, every ratio, and their best and worst post. It does not rank them — "how often they land one" and "how big it is when they do" usually disagree, and a hit rate over a short window is one post either way, so the window travels with every number and the read is yours.
watchlist_standings	2 per watched creator	The same, across everyone on your watchlist: "who moved" in one call instead of one per creator. Reading the watchlist is free; the fetches are not, so it confirms the total before spending. catch_up_watchlist answers what is new; this answers how it did.
who_should_i_work_with	2, or 4 with a seed	A collaboration shortlist: a keyword search merged with the lookalikes of a creator who already fits, marked by which search found each one. Every candidate also carries the links pulled out of their bio, typed and sorted by how much opening one will tell you — a repository to read the code in, their own site, a link hub that holds the real links — so vetting is reading the work rather than re-reading the follower count. Those links are never fetched here: they came out of a field the person being evaluated controls, so the host opens them, and the result says so. It does not measure audience overlap — that costs about nine credits a candidate, so the result says so and shows how to check a finalist rather than faking the signal.
find_people_with_problem	6 per platform	Find people describing a problem in their own words — prospects, early users, anyone whose complaint your product answers. Say the problem the way a person would say it and it searches for posts shaped like that complaint rather than for the topic it is about: searching the topic finds marketing about it, which is what discover_social_posts is for. Widens the query into the forms a complaint takes — the plain phrasing, the does-anyone-else question, the is-there-a-tool ask — and marks which one surfaced each post. A wide cheap net and not a filter: the posts come back for you to judge, and plenty will be off-target. Defaults to reddit, where people describe workflow pain in sentences. Each platform is searched once per query shape — three by default, at 2 credits a search. Not searchable here: linkedin, which has no keyword post search upstream. Pass verbatim: true to have every item rendered whole for quoting rather than clipped to fit; same price.
show_collab_shortlist	free	Draws the candidates you scored, ranked, and asks the user which to approach. The scores are attributed to your model, not presented as a nooticr rating, and a candidate scored without anything having been opened is marked unverified. Makes no requests.
why_did_this_underperform	3	One post against the creator's own recent distribution, with the post taken back out of its own baseline. Returns median, quartiles, ratio and percentile, so the answer can be "this is an ordinary result, not a failure". Different question from compare_social_posts, which weighs two URLs you already picked.
what_should_i_make_next	2 + 2 per post read + 2 (12 by default)	Demand against supply: what your commenters explicitly ask for, set beside what a niche sweep shows is already being made. A gap nobody asked for is noise; a request nobody serves is the opportunity. Falls back to your most-used hashtag when you name no niche.
detect_spoken_mentions	2	Every brand named out loud in one video. The spoken transcript and the written caption, side by side, for your model to list each brand with its verbatim sentence, the creator's framing and whether it was said, written or both. Works where the creator never enabled captions, because the audio is transcribed. 1 credit for get_post_transcript plus 1 for get_social_media; nothing when no transcript can be made.
search_spoken_mentions	2 per platform narrowed by niche + 2 per creator handle + 1 per transcript, up to maxTranscripts	The mirror of search_mentions, for what was said rather than typed. Narrows to candidate posts (a niche sweep, named handles, and/or your watchlist), transcribes only the most-viewed survivors up to a hard ceiling, and searches the words for the term — TikTok, YouTube and Douyin, and only where the creator actually enabled captions. Reports how many candidates were found, transcribed and matched, so the spend is legible.

Study a market on Amazon

Everything above reads social content. These read a marketplace — the place where the same audience says what it actually bought and why it sent it back. The question they exist for is a category decision: we are considering ashwagandha — what makes people buy, what stops them, what are the incumbents good at, and where is the gap? Every one of those answers lives in review text, so the unit of work is a scan of the category rather than a lookup of a product.

Collection is genuinely slow, and honestly so: each listing is a real browser render behind Amazon's bot defences, and the pace between them is set by what Amazon tolerates rather than by what the machine can do. Ten listings with their reviews is minutes, not seconds — so a scan returns what is ready and hands back a scanId for the rest.

Tool	Credits	What it is for
scan_amazon_category	3 per listing collected (30 by default)	The category, collected: listings with prices, star histograms, Amazon's own review-aspect counts and the review text itself, plus the arithmetic over them — price spread, rating spread, which aspects recur across how many brands, and the share of each listing's ratings sitting at 1-2 stars, read from the histogram rather than from the handful of reviews a scrape returns. Pass query to search the category, asins to pin the competitors a customer named, or both; named competitors keep their place at the front. Because the price is set by an argument, it confirms the total before spending it.
amazon_scan_status	free	Picks a running collection back up by scanId — the listings collected since, with the same rollup. Free: it is the poll scan_amazon_category asked for, and billing the second half of one answer would charge for a wait the server chose.
get_amazon_product	3	One listing by ASIN or URL: price, rating, the full star histogram, features, specs, Amazon's review digest and aspect breakdown, and the reviews.
scan_marketplace_category	3 per product collected (30 by default)	The same category read on eleven more sites — AliExpress, Cdiscount, Flipkart, Mercado Libre, Lazada, Otto, Rakuten, Trendyol, Jumia, Vinted and Temu. Vinted is resale rather than retail, so its rating describes the seller and the scan says so in ratingScope — do not read it as a product's reviews. marketplace picks the site; market picks the storefront for the sites that have several, and the argument names the real set per site so a model cannot send Otto a Lazada region. Same rollup, same confirmation before spending.
marketplace_scan_status	free	Picks a running marketplace collection back up by scanId and the same marketplace. Free, for the same reason amazon_scan_status is.
get_marketplace_product	3	One product from any of the twelve sites, by id or URL: price, rating, the full star histogram, specs and the reviews.
show_amazon_category_insights	free	Draws the read your model wrote — purchase drivers, barriers, what each brand does well, the gaps, the positioning angles — beside the listings and their reviews, so a person can click a product and check any claim against the text it came from. The conclusions are attributed to your model, never presented as a nooticr rating of anyone's product. Given the scanId, it re-reads the listings from the scan itself (free) instead of drawing whatever the model re-sends, and flags any ASIN the read cites that the scan does not contain; without one, the card says its listings were not checked.
show_marketplace_category_insights	free	The same card for the other eleven marketplaces: your read beside that site's listings and reviews, labelled with the right site, re-read from the scan by scanId and marketplace so nothing is re-typed.

Make something