# Docs

- Getting started
  - [Quickstart](/getting-started/quickstart): Five minutes from a fresh API key to your first dataset and mentions list — in Python.
  - [Concepts](/getting-started/concepts): Dataset, run, mention, audience dataset, profile, pattern — the words that come up everywhere in the API and MCP.
  - [Authentication](/getting-started/auth): API keys for REST + MCP — minting, sending, rotating, and what team-member semantics look like.
  - [Pricing](/getting-started/pricing): How buzzabout charges per API call — a dollar balance, four categories, all per-result and all reservation-based.
- Tutorials

  - **Get started**
  - [Run your first research](/tutorials/run-your-first-research): Go from a blank home screen to your first set of earned-media mentions in the app, then ask the assistant a follow-up question or apply a skill.
  - [Set up your project](/tutorials/set-up-your-project): Create a project, set up its brand context, and reuse a collected dataset across every chat so AI responses are grounded in your brand.

  - **Features**
  - [Pattern analysis](/tutorials/analyse-patterns): Pattern analysis is the technique under most of buzzabout's research — point a question at a dataset and it discovers an emergent taxonomy of clusters along the one axis you choose, from pain points to CTA types to tone of voice.
  - [Use skills](/tutorials/use-skills): Apply a saved, expert instruction block to any chat so the AI assistant analyses your mentions a consistent, repeatable way — and browse the built-in skills catalog.
  - [Create custom skills](/tutorials/create-custom-skills): Save a reusable research instruction once as a custom skill, then apply it to any chat or project in a single click.
  - [Use smart parameters](/tutorials/use-smart-parameters): Turn any question you have about your mentions into an AI-filled column that runs your prompt against every post and stores the answer.
  - [Use insights](/tutorials/use-insights): Save the sharpest lines from an AI reply as insights so the assistant builds on them, then reuse them as context across new chats in a project.
  - [Export mentions as CSV](/tutorials/export-mentions-csv): Select rows in the Library mentions table and export them to a CSV that includes every column and Smart Parameter — ready to hand off to analysts or pivot in a spreadsheet.
  - [Share & export assets](/tutorials/share-and-export-assets): Download an AI-assistant report or chart as PNG or PDF, or share it with a public link or live embed.
  - [Set up a listening agent](/tutorials/set-up-a-listening-agent): Create an agent that re-runs your datasets' queries on a schedule, grows your Library, and delivers a focused run digest to Slack, a webhook, email, or its own chat.
  - [Connect MCP to Claude](/tutorials/connect-mcp-to-claude): Wire the Buzzabout MCP server into Claude using OAuth for interactive use, or an API key for headless agents.

  - **Use cases**
  - [Brand research](/tutorials/brand-research): Collect the public posts, comments, and community chatter about your brand into a dataset you can question — then read the earned media that shapes how people see you.
  - [Competitor research](/tutorials/competitor-research): Scope the standard research flow to competitor keywords or profile URLs, group the datasets in a project, then analyse share of voice, audience, and trends with skills.
  - [Niche research](/tutorials/niche-research): Turn one focused niche dataset into pain points, objections, the market's tone of voice, volume and engagement, and per-network market share — then keep it live with a listening agent.
  - [Run audience analysis](/tutorials/run-audience-analysis): Build an audience dataset from a collected dataset, then read demographics, interests, brand affinities, creator tier, and an inferred Personality (OCEAN) fingerprint for the people behind the posts.
  - [Analyse pain points](/tutorials/analyse-pain-points): Turn a collected dataset into a ranked map of what your buyers actually complain about, using the built-in Analyze pain points skill.
  - [Analyse feature requests](/tutorials/analyse-feature-requests): Apply the Feature requests skill to a collected dataset to turn social chatter into a severity-ranked, triaged product backlog.
  - [Track share of voice](/tutorials/track-share-of-voice): Benchmark your brand against competitors by share of the conversation, with per-brand sentiment, using the Mentioned brands skill or an assistant ask.
  - [Spot trends](/tutorials/spot-trends): Apply the What's trending skill to a collected dataset to catch rising topics early and ride them this week.
  - [Generate a content brief](/tutorials/generate-a-content-brief): Run your analysis in one chat thread — pain points, feature requests, content ideas, a content gap analysis — then ask the assistant for a content brief that pulls it all together into a shareable publishing plan.
  - [Create a mention view](/tutorials/create-a-mention-collection): Use the Library's saved Views to filter, sort, and combine your collected mentions — then export them to CSV.
  - [Create a watchlist](/tutorials/create-a-watchlist): Keep a fixed set of competitors, influencers, subreddits, or URLs under continuous watch by building a URL-based dataset and attaching a listening agent focused on what matters.
- MCP
  - [MCP overview](/mcp/overview): How buzzabout exposes itself as an MCP server — the assistant flow, the read tools, and how to authenticate.
  - [Use in your agent](/mcp/use-in-your-agent): Wire buzzabout's MCP server into any MCP-capable client — Claude, Claude Code, Codex, Cursor, ChatGPT, or your own SDK-built agent.
  - [Authentication](/mcp/auth): Which auth method to use per MCP client — OAuth for standard assistants, x-api-key for Claude Code, Codex, and Cursor.
  - Tools
    - [Tools reference](/mcp/tools/reference): Every buzzabout__* MCP tool — the assistant flow (ask / get_message / render) plus read-only lookups.
- API reference
  - [API overview](/api/overview): REST surface map — base URL, response envelope, error model, pagination, and account scoping.
  - [Run your first analysis](/api/first-analysis): End-to-end in Python — scrape posts, collect audience profiles, then hand the dataset to the AI assistant.
  - [Rate limits and quotas](/api/rate-limits): How buzzabout regulates traffic — per-key request limits, balance pre-checks, in-flight caps, and the 429 envelope your client needs to handle.
  - [Reference types](/api/reference-types): The references[] array on assistant messages — entity refs plus structured post citations.
  - Endpoints

    - **Data collection**
    - [Datasets](/api/endpoints/datasets): Eight endpoints — full CRUD on dataset containers plus async scraping runs.
    - [Research previews](/api/endpoints/research-previews): Sample a search and estimate its volume before committing spend to a full dataset run.
    - [Audience datasets](/api/endpoints/audience-datasets): Audience datasets surface the people behind your mentions — eight endpoints, CRUD plus async profile-collection runs.
    - [Tracking agents](/api/endpoints/tracking-agents): Nine endpoints — manage agents that re-scrape on a schedule, watch for narrative shifts, and emit alerts.

    - **Data access**
    - [Mentions](/api/endpoints/mentions): POST /v1/mentions — global, filterable, paginated mentions across one or more datasets.
    - [Audience profiles](/api/endpoints/audience-profiles): POST /v1/audience_profiles — global, filterable, paginated profiles across one or more audience datasets.

    - **Analysis**
    - [Pattern detections](/api/endpoints/pattern-detections): Kick off and poll cross-mention pattern discovery against a dataset.
    - [Patterns](/api/endpoints/patterns): Read a discovered pattern and walk into its constituent items + posts.
    - [Custom parameters](/api/endpoints/custom-parameters): Nine endpoints — define bespoke LLM-extracted fields, preview, and run them across a dataset.

    - **Assistant**
    - [Ask (assistant)](/api/endpoints/ask): POST /v1/ask — hand a prompt to the buzzabout assistant, get markdown back plus typed references.
    - [Chats](/api/endpoints/chats): Two read endpoints — chat metadata and paginated message history for a single chat.

    - **Account**
    - [Account (me)](/api/endpoints/me): Two endpoints — current account / plan / seats, and a paginated usage-history feed in dollars.
    - [Prices](/api/endpoints/prices): GET /v1/prices — your account's price in US dollars for each of the four public categories.