@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:

Code

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

Claude Code / CLI without the plugin:

Terminal

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, …):

config.json

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

Tools

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

Seven of them — the ones 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. 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.
`analyze_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.
`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_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_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.
`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.
`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. 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.
`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.
`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, because no nooticr connection carries comment-write permission on any network. `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`	2 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.
`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_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.
`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.

Make something

Tool	Credits	What it is for
`write_hooks`	2, or free	The source post and its transcript, to write openings against. Give a topic instead of a url and it fetches nothing and costs nothing.
`show_hooks`	free	Draws the hooks your model wrote from `write_hooks` — each one with the device it uses and who it stops. Makes no requests; it only renders what you pass it.
`score_draft`	free	Your draft back with the rubric to hold it to — hook, clarity, payoff, specificity and fit, each scored 1-10, plus the three fixes worth making and a rewritten opening. Fetches nothing: the text is already yours. The only tool that runs before the content exists.
`repurpose_post`	2	The source post and its transcript, to rewrite for other surfaces — X thread, LinkedIn post, carousel slides, YouTube metadata, newsletter.
`show_repurposed_post`	free	Draws the rewritten copy your model produced from `repurpose_post` — one entry per surface. Makes no requests; it only renders what you pass it.
`create_variants`	2	The post that worked, with its transcript, to build variants from: hook, the angle that changes, ordered shot beats and a CTA.
`show_variants`	free	Draws the variants your model wrote from `create_variants` — each one's hook, angle, beats and CTA. Makes no requests; it only renders what you pass it.

Account

Tool	Credits	What it is for
`check_nooticr_credits`	free	Balance and billing URL.
`nooticr_login`	free	Re-link the account when a call fails with an authentication error.
`list_social_connections`	free	What you have connected and what each connection is actually allowed to do — read the account, publish a post, manage comments — as yes, no or unknown. Unknown means the grant predates scope recording: treat it as "try it", not as a refusal. Also says which platforms can be linked at all, which is a smaller set than the networks nooticr reads.
`connect_social_account`	free	A link the user opens to connect one account. They approve at the provider in their own browser; nothing is connected until they do, and no credential passes through the tool or the model's context. Each call mints a fresh link — an old one does not work twice.
`create_product`	free	Create a product in your own workspace — the row every other own-account tool needs before it has anything to work with. Always creates in the calling session's workspace, never one you could name. `name` and `slug` are required; every other field is snake_case and read by exact key, so a camelCase `websiteUrl` lands nowhere. Writes no brand playbook.
`update_product`	free	Patch a product's fields. An omitted argument leaves its column unchanged, so this cannot blank a field by not mentioning it, and the result lists what was actually written — a misspelled field shows up as one that did not change rather than as a silent no-op.

Your own product

Everything above reads someone else's content. These read and generate for your own — a different product, a different balance (your workspace's plan AI credits, not the personal MCP credits above), billed exactly like the matching dashboard button.

Tool	Credits	What it is for
`list_own_apps`	free	Every product in your workspace — call this first when you have more than one and another tool below asks for `appId`.
`create_product`	free	Create a new product in your workspace — `name`/`slug` required, plus optional `description`, `website_url`, `niche`, `product_type` and store-listing fields (snake_case — see note below). Takes no workspace id; always creates in your own. Does not generate a brand playbook — call `analyze_product` for that.
`update_product`	free	Patch a product's fields — omitted arguments leave their column unchanged. Same snake_case field names as `create_product`.
`get_scheduled_posts`	free	Your own scheduled and draft posts in the content pipeline — title, status, scheduled time, approval status. What is queued to publish.
`get_post_performance`	free	Your own already-published posts with their engagement counters — views, likes, comments, shares, platform, post date. The raw performance history, not an interpretation of it; pair with `growth_brief` for that.
`get_video_stats`	free	Your own most recently synced video performance stats across every connected creator — views, likes, comments, shares, plus a running total. Reads the last sync; does not trigger a new one.
`get_content_plan`	free	The saved weekly content plan for a product, if one has been generated. `plan: null` when none has.
`get_brand_playbook`	free	Your product's brand playbook — name, description and the playbook text — if one has been configured, in the dashboard or by `analyze_product`. Read-only; `available: false` when none exists.
`review_post`	free	Score a draft before you publish it — hook strength, an A/B hook comparison, aesthetic and storytelling notes, rewritten hooks/captions. Never billed, same as the dashboard's own pre-publish review.
`draft_post`	plan AI credits	A ready-to-use draft (title, caption, hashtags, per-slide script) for a topic, grounded in your product's name. Returns text only — saves nothing.
`growth_brief`	plan AI credits	A plain-language brief — the one insight that matters, wins, risks, next actions — grounded in your real post history and synced analytics.
`generate_content_plan`	plan AI credits	A one-week, day-by-day content plan for your creators, grounded in what already worked. Saved — fetch it later with `get_content_plan`.
`generate_captions`	plan AI credits	Timed on-screen caption cues for a video — a transcript plus start/end-timed lines. Returns cue data only.
`analyze_product`	plan AI credits	Start an AI analysis of your product: fetches an excerpt of its own website (a real outbound fetch, not just nooticr's own data), reads its posts and fleet performance, and writes the result as its brand playbook. Runs in the background — returns a `jobId`, poll it with `analyze_product_status`. 10 plan AI credits, first analysis free per workspace.
`analyze_product_status`	free	Poll a job `analyze_product` started. Returns `state` (pending/thinking/done/error) and, once done, the analysis. Free — the cost was already charged when the job started.
`list_social_connections`	free	The social accounts you've connected and what each is allowed to do — read, publish, manage comments. Also lists which platforms can be connected at all.
`connect_social_account`	free	A link to open to connect one account. You approve at the provider; nothing connects until you do, and no credential ever passes through this tool.

How billing works

New accounts get 20 free credits.
Every tool is priced at what it fetches upstream, and bills from the first call. A tool that fans out to two fetches costs both — analyze_post is 2 for the frames plus 1 for the transcript — and its description says so. There is no free first use: that grant belonged to the AI calls, and there are none.
A call that fails is refunded automatically, and a call interrupted mid-flight is billed once at most — retries are idempotent.
Platform admins bypass credit debiting entirely.

Interactive cards in Claude / ChatGPT chat

Every tool that returns posts also renders inline interactive cards directly in the chat (MCP Apps ui://nooticr/view resource rendered in a sandboxed iframe):

Video posts (TikTok/IG/YouTube/Douyin/LinkedIn) — an inline <video> player with the thumbnail as poster, playing the re-hosted permanent MP4 (no expiring CDN tokens).
Carousels / slideshows — a horizontally scrollable strip of every slide with an image count chip.
Single images — inline thumbnail.
Text-only posts (LinkedIn / X) — a styled quote block of the post text.
Official brand marks — each card shows the platform's real logo (simple-icons) in its brand color instead of an emoji.

search_mentions renders a different view, because monitoring is triage rather than browsing:

Each row is a person saying something. Their picture, with the network's mark on it, then the handle and when they wrote it — "today at 7:25 PM", not a timestamp — then the comment with the term highlighted everywhere it appears, then what it earned in likes and replies. A ×N badge marks a comment that names the brand more than once.
Each post carries its reach. The same sentence under a 25K-upvote thread and under a post nobody saw are not the same problem, and nothing else on screen tells you which one you are reading.
The per-network counts are filters. Click one to narrow to it; a network that answered with nothing is shown but is not clickable, because filtering to it is a dead end. Filtering and sorting redraw from what you already paid for — the view never re-queries.
A burst collapses. One post with a run of near-identical replies (a coordinated fan campaign, say) shows the first few and offers the rest, so it cannot push four other networks off the screen.
Comments are selectable, and the selection is agentic. Tick any comments, or select a whole thread at once, and send them — the view hands the host the comment ids the tool issued, so the model can reply to, escalate or analyse exactly those. Selections survive filtering and sorting.
Load more pages from the nextOffset the tool returned.

Also inline thumbnails render for the model / plain-text clients:

get_social_media / understand_social_post / analyze_post — up to 4 frames inline (poster + carousel slides). The full mediaItems[].preview_url + thumbnailUrl stay in structuredContent for the model to reason over.
discover_social_posts / get_user_posts / analyze_creator_profile — each returned post shows its thumbnail inline (up to 4 at once) together with its title/caption + views/likes/comments. Say "next" or "show more" — Claude will re-call with offset/limit pagination. Say "analyze the 2nd one" — Claude calls analyze_post or understand_social_post on that URL.
Batch analysis — ask "analyze all 4" or "understand these 3 in batch" and Claude will call analyze_post/understand_social_post once per URL in parallel and summarize. For large batches, discover_social_posts + a follow-up analyze_post per URL is the recommended flow.

Your own model does the thinking

There is no other mode. Every tool returns the material an analysis would have been built from, at the price of the fetch, and asks your model to reason over it. Nothing here calls a model of ours, so nothing here sells you a judgement.

For the visual tools that means actual frames — real image content blocks, not a description of them — paired with the transcript. Measured on Claude Code: ~1,200 tokens per 1280×720 frame, eight frames read back correctly and in order. Twenty frames is about 2.4% of a million-token context.

Tool	What comes back	Credits
`analyze_post`	frames as images, plus the transcript and stats	3 (2 + 1)
`analyze_comments`	the comments, each with an id, and the labels to use	2
`analyze_creator_profile`	the creator's recent posts and their numbers	2
`score_draft`	your own draft, and the rubric to score it against	free

The prices above are derived from the fetches each tool makes rather than written down twice — see EVIDENCE_PLANS and planCost in src/shared/evidence.ts. score_draft has no plan at all: it reviews text you already have, so there is nothing to fetch and nothing to charge for.

show_comment_review closes the loop: hand back your classifications and it draws them — every comment with its sentiment and category, filterable and selectable. Free, and it makes no requests.

Before an expensive call, it asks

Most tools print their price in their own description, so a call costs what you already read. Six do not, because their price is set by an argument — or by a list the request never mentions:

search_mentions bills per network swept, so a bare "monitor my brand" sweeps all nine for 21 credits.
catch_up_watchlist bills per creator, so the price is the length of your watchlist.
answer_my_audience and what_should_i_make_next bill per post opened (14 and 12 credits at their defaults), so they ask once the real post count is known rather than on a worst case.
search_spoken_mentions bills per network, per handle and per transcript, so it asks on the worst case it could reach.
create_brand_watch asks however small the number is, because what is being agreed to is a recurring charge rather than one run.

Above 6 credits those first five ask first, over MCP elicitation — the client shows the number and you accept or decline. Declining spends nothing and is not an error. A client that does not support elicitation is not blocked; the call runs as it always did.

Two rules go further than the threshold. A scheduled watch always asks, because the standing arrangement is the thing being approved. And a watch that emails a digest refuses to accept a deliverTo address unless a person actually saw the dialog and said yes — "nobody could be asked" is a fine answer for a spend and the wrong one for an address.

Prerequisites

Node.js >= 18 (tested on Node 22)
An nooticr account (created automatically on first sign-in — Google sign-in via npx @nooticr/mcp login; the dashboard is not required: the server auto-creates a default workspace + app for new accounts)
Access to an nooticr server — defaults to the cloud API (https://api.nooticr.com); point NOOTICR_BASE_URL at your own deployment for local development

Quick start

bash

# 1. Sign in with your nooticr account (Google sign-in opens in your browser)
npx @nooticr/mcp login
#    or with email/password:
npx @nooticr/mcp login --email you@example.com --password '...'

# 2. Add it to your MCP client (see install section above) — or run it manually:
npx @nooticr/mcp            # stdio (default; for Claude Desktop / Cursor)
npx @nooticr/mcp --http     # remote HTTP with OAuth (for OpenAI Agents SDK)

login stores your nooticr tokens in ~/.config/nooticr-mcp/credentials.json (mode 0600). If your browser cannot be opened automatically, copy the URL it prints into a browser manually.

Usage in Claude Desktop

After npx @nooticr/mcp login, add to claude_desktop_config.json:

config.json

{
  "mcpServers": {
    "nooticr": {
      "command": "npx",
      "args": ["-y", "@nooticr/mcp"]
    }
  }
}

Tokens are resolved from the credentials file written by login (or from NOOTICR_ACCESS_TOKEN). If your client does not inherit your shell environment, set the env vars explicitly:

config.json

{
  "mcpServers": {
    "nooticr": {
      "command": "npx",
      "args": ["-y", "@nooticr/mcp"],
      "env": {
        "NOOTICR_BASE_URL": "http://localhost:8080"
      }
    }
  }
}

Usage in Cursor

In .mcp.json (project root) or ~/.cursor/mcp.json:

config.json

{
  "mcpServers": {
    "nooticr": {
      "command": "npx",
      "args": ["-y", "@nooticr/mcp"]
    }
  }
}

After adding the server, run npx @nooticr/mcp login in your terminal — Cursor spawns the server with your environment, so it picks up the stored credentials.

For remote/HTTP usage, Cursor can connect to the OAuth-enabled HTTP mode with npx @nooticr/mcp --http running, pointing the server URL at http://localhost:3457/mcp. Remote clients (including Cursor) discover the OAuth endpoints from http://localhost:3457/.well-known/oauth-authorization-server, open the "Sign in with Google" page, and store the resulting access token.

Usage with OpenAI Agents SDK

Start the HTTP transport:

Terminal

npx @nooticr/mcp --http --port 3457

Python:

server.ts

from agents import Agent, Runner
from agents.mcp import RemoteMCPClient

async def main():
    async with RemoteMCPClient(
        url="http://localhost:3457/mcp",
        auth_provider="oidc",  # OAuth flow opens your browser once
    ) as client:
        agent = Agent(name="nooticr", mcp_servers=[client])
        result = await Runner.run(
            agent,
            "Analyze this video: https://www.youtube.com/watch?v=dQw4w9WgXcQ",
        )
        print(result.final_output)

TypeScript (Agents SDK):

server.ts

import { Agent } from "agents";
import { RemoteMCPClient } from "agents/mcp/client";

const client = new RemoteMCPClient({
  url: "http://localhost:3457/mcp",
  authProvider: "oidc", // opens the browser for the OAuth flow
});

const agent = new Agent({
  name: "nooticr",
  mcpServers: [client],
});

const result = await agent.run(
  "Analyze this video: https://vm.tiktok.com/abc123/",
);
console.log(result.output);

For a local stdio process with the Agents SDK, use StdioMCPClient (Python: StdioMCPClient(command="npx", args=["@nooticr/mcp"])).

Command line

Code

nooticr-mcp                    Start in stdio mode (default transport)
nooticr-mcp --stdio            Same as above
nooticr-mcp --http [--port N]  Start the remote HTTP transport with OAuth (default port 3457)
nooticr-mcp login              Sign in to nooticr via Google in your browser
nooticr-mcp login --email ... --password ...   Password login
nooticr-mcp --help             Show help

Environment variables

Variable	Default	Description
`NOOTICR_BASE_URL`	`https://api.nooticr.com`	nooticr server base URL (trailing slash stripped)
`NOOTICR_ACCESS_TOKEN`	—	nooticr JWT access token; takes priority over the credentials file
`NOOTICR_CREDENTIALS_FILE`	`~/.config/nooticr-mcp/credentials.json`	token store path
`NOOTICR_PUBLIC_URL`	`http://localhost:3457`	public base URL advertised in OAuth metadata (HTTP mode)
`NOOTICR_PORT`	`3457`	port for `--http` and `login`
`NOOTICR_TRANSPORT`	`stdio`	`stdio` or `http` (same as `--http`)

How authentication works

stdio mode (Claude Desktop, Cursor): the server uses the token from NOOTICR_ACCESS_TOKEN or the credentials file written by login. If the token is expired it is automatically refreshed with the stored refresh token, and if the nooticr API returns 401 the request is retried once after a refresh.

HTTP mode (OpenAI Agents SDK, remote clients): the server runs its own OAuth 2.0 authorization server (Authorization Code + PKCE S256, public client, per the MCP 2025-03-26 spec):

GET /.well-known/oauth-authorization-server — metadata
GET /authorize — validates the request (loopback or https redirect URIs) and forwards the browser to nooticr's Google sign-in
GET /oauth/callback — our own loopback callback; exchanges nooticr's completion code for nooticr JWTs and redirects back to the MCP client with a one-time code
POST /token — verifies PKCE and issues an opaque Bearer token bound to the nooticr session (valid 1 hour)
every MCP RPC validates the Bearer token against the session map

Supported URLs

TikTok: tiktok.com/*, vm.tiktok.com/* (and www./m. subdomains)
Instagram: instagram.com/* (reels, posts, carousels), instagr.am/*
YouTube: youtube.com/* (including /shorts/), youtu.be/*, m.youtube.com/*
X: x.com/*, twitter.com/*
Reddit: reddit.com/*, redd.it/*
Weibo: weibo.com/*, weibo.cn/*
Douyin: douyin.com/*
Xiaohongshu: xiaohongshu.com/*, xhslink.com/*
Bilibili: bilibili.com/*, b23.tv/*
LinkedIn: linkedin.com/* (posts, profile URLs)

All tools accept these hosts and handle video, image, carousel, slideshow and text posts.

Troubleshooting

Not authenticated with nooticr / 401: run npx @nooticr/mcp login or set NOOTICR_ACCESS_TOKEN.
402 paywall / insufficient MCP credits: your nooticr account is out of credits. Prices are listed per tool in Tools — 1 credit for a post lookup or transcript, 2 for discovery and for a tool that makes one fetch, 3 for the two that fetch frames and transcript. Every tool bills from the first call. Top up via check_nooticr_credits, or the nooticr dashboard at https://nooticr.com/settings?tab=billing.
Expired refresh token: the stored refresh token was rejected by the nooticr server. Run npx @nooticr/mcp login again to re-authenticate.
Could not reach the nooticr server: NOOTICR_BASE_URL is unreachable or wrong.
Client shows "Bad Request" or connection errors in HTTP mode: make sure the port matches NOOTICR_PUBLIC_URL and that the client fetched a token first (the OAuth flow must complete once in your browser).

Platform submissions

See docs/SUBMISSION.md for ready-to-paste configs and the submission package for Claude Desktop, Cursor, and OpenAI Agents SDK.

Security notes

The credentials file is written with mode 0600 and the directory with 0700.
OAuth redirect URIs are restricted to loopback (http://localhost, http://127.0.0.1, http://[::1]) or https:// URLs; the /authorize endpoint requires PKCE (S256).
Authorization codes and PKCE challenges are one-time use and short-lived (in-memory).
Access tokens are opaque, random, and bound to the in-memory session map; they expire after 1 hour. Restarting the server invalidates all sessions.
Never share your credentials file or NOOTICR_ACCESS_TOKEN.

Development

Terminal

npm install
npm run build    # tsc
npm test         # vitest (66 unit tests, mocked fetch — no network)

Those are unit tests against a stub backend. For real MCP-protocol end-to-end tests — no Rust, no Postgres, no API key — npm run test:e2e-smoke:fixture spawns this repo's real built CLI and drives it over stdio against a pure-Node stand-in for nooticr-server. See docs/testing/agentic-e2e-testing.md for that plus the tier above it (npm run test:e2e-smoke against a real nooticr-server). For a real model driving the server through whole journeys — asserting on the chain of tools it walks, not on one call — see npm run test:quests and docs/testing/tool-chaining-quests.md. And for tests/e2e/agentic-visual*.e2e.ts — every reachable widget view, driven by a real tool-call result, rendered in a real browser with every host-facing button actually clicked (npx playwright test tests/e2e/agentic-visual.e2e.ts tests/e2e/agentic-visual-full-app.e2e.ts). That exercise found six real product bugs, documented in the same doc.

License

MIT