Major x Snitchfeed: Official API and MCP Connectors for Social Listening
Snitchfeed's official API and MCP connectors are live on Major. Run keywords through an SEO app into a live CMS post, turn signals into LinkedIn posts via Taplio, draft Reddit AEO replies for a person to post, and listen to named accounts on LinkedIn and X.
Social listening usually dies in a Slack channel: mentions pile up, nobody scores them, and the useful ones get buried by brand spam. Snitchfeed is built to avoid that. You describe an ICP or a query, and it scans LinkedIn, X, Reddit, Bluesky, and Hacker News, scores each match for relevance, intent, and sentiment, then hands you the ones that look like demand.
The official Snitchfeed API and MCP connectors are now live on Major. An agent can set up the listeners, and an app can poll the mentions, keep the ones above your fit-score cutoff, and notify an owner. We already run this internally for brand and competitor listening, and to feed live language into SEO keyword picks. Any team on Major can now build the same setup.
Key takeaways
- Snitchfeed's official API and MCP connectors are live on Major, covering LinkedIn, X, Reddit, Bluesky, and Hacker News.
- Put the poll, score, store, and notify loop in an app, and keep the model on which accounts, queries, and thresholds to watch.
- If you do not have a Snitchfeed workspace yet, sign up for Snitchfeed, then attach the connector on Major.
Five workflows to start with:
- Snitchfeed keywords through an SEO app into a live CMS post
- Social trend and keyword extraction
- LinkedIn posts from live signals via Taplio
- Reddit AEO replies that a person posts by hand
- ABM listening on LinkedIn and X
What Snitchfeed actually does
Snitchfeed is social listening pointed at intent. Listeners watch public posts, and each mention comes back with intent tags, a fit score, sentiment, and a link to the source. You can route those into Slack, email, a webhook, or a client that speaks HTTP or MCP.
The difference from a saved search is the scoring. A raw LinkedIn search for "looking for a tool" is mostly noise; Snitchfeed keeps the posts that look like a buying committee asking, and drops the rest.
The official API and MCP connectors
HTTP API
The Snitchfeed HTTP API sits at api.snitchfeed.com/v1 behind a Bearer token. Reading your org's mentions, listeners, feeds, and usage does not spend search credits.
Metered search covers:
- X
- LinkedIn posts and people
- LinkedIn company pages
- Hacker News
Rate limits are 20 requests per minute per search surface, with at most two in-flight requests across v1.
MCP server
The Snitchfeed MCP server is at api.snitchfeed.com/mcp. Auth is OAuth, so there is no API key to generate or paste. It works with Claude, Cursor, ChatGPT, and other MCP clients, and as a Major connector an app can call on a schedule.
It exposes 42 tools, including:
- Read mentions
- Manage listeners
- Build saved feeds
- Run ad hoc search, including LinkedIn person posts, comments, and reactions
- Pull analytics
Use MCP when an agent should operate the workspace in language:
- Create a listener
- Tighten a noisy query
- Ask for unseen high-fit mentions from the last day
Use the HTTP API when an app should poll the same data on a cron, write rows into a managed database, and fan out Slack or CRM updates without another reasoning pass.
Five workflows to build first
SEO from live keywords to a published page
Snitchfeed keywords land in an SEO app on Major, then a chain of agents takes them live. Each agent does one job, and the app holds the keyword, the brief, the draft, and the live URL so the same theme does not get written twice.
- Snitchfeed keywords write into the SEO app.
- A research agent scores them for volume and keeps the ones worth a page.
- A brief-builder agent writes the SEO brief in the same app.
- A writer agent turns the brief into a draft, still in the app.
- A publisher agent, connected to the CMS, takes the draft live.
Answer engines cite pages that match how people actually ask, which is why the keywords start in public conversation instead of a planning spreadsheet.
Social trend monitoring and keyword extraction
Volume, intent tags, and wording drift week to week. A scheduled agent asks Snitchfeed for mention volume by platform, the top intent tags, and the phrases that keep recurring, then writes those keywords into a workbench a writer can open tomorrow.
This is the loop we already run internally: Snitchfeed listeners feed specific themes into daily SEO keyword picks. The useful output is not a dashboard screenshot. It is a growing list of phrases buyers used this week, with links back to the posts.
LinkedIn marketing from live signals
The same signals can feed a LinkedIn calendar, not just a keyword sheet.
- A scheduled agent pulls high-fit mentions from Snitchfeed.
- It clusters them into topics a founder or marketer could actually post about.
- A content agent drafts each LinkedIn post in the house voice.
- Taplio schedules the post.
The handoff is the point: Snitchfeed does not write the post, the content agent does not scrape LinkedIn, and Taplio does not invent the topic. Each step does one job, and a person can still kill a draft before it goes out.
Reddit listening for AEO replies
Reddit threads are where people ask the follow-up questions answer engines later cite. Snitchfeed listens to those threads, and an agent writes a reply draft against each topic.
The app stores each draft with:
- The source thread
- The suggested answer
- A status of ready or skipped
A person publishes the reply. Auto-posting to Reddit is disallowed because it violates Reddit's anti-botting policy and can get the account banned. The app is a queue of drafts, not a poster.
ABM account listening on LinkedIn and X
Named-account listening is a different query than brand monitoring. Point listeners at the companies on your target list and at people in the buying committee.
Snitchfeed can:
- Search LinkedIn posts
- Pull posts and comments by a person from a profile URL
- Scrape comments and reactions on a given post
- Search X for the public conversation around those same accounts
The agent decides which accounts and people belong on the list, and which intent tags count as a sales-ready mention:
- Asking for a tool
- Comparing vendors
- Complaining about a competitor
The app polls, scores, and dedupes, then drops high-fit rows into a prospecting queue or CRM. A human still takes the meeting. They should not be the one grepping LinkedIn at 7am.
What belongs in the app
The agent reasons about the setup: queries that do not collide with noise, a fit-score threshold, owners, and a destination. Then it builds an app that:
- Polls mentions or runs a scoped search on a schedule.
- Stores mention id, platform, author, URL, intent tags, fit score, and raw text in a managed database.
- Notifies Slack or email only above the threshold, with a link back to the source post.
- Exposes a queue a person can mark seen, assigned, or dismissed, with an audit log of every write.
Leave judgment in the agent: which new account to add, whether a spike is a real shift or a one-off thread, and when a query is too noisy. Do not leave the poll-and-store loop in the model. That work is the same every run, so it belongs in code.
Get started
Two steps, in this order. The first is on Snitchfeed; the second is on Major.
- Create a Snitchfeed workspace. Sign up for Snitchfeed. Paid tiers include the API and MCP. Start with one listener you actually care about: a category question, a competitor name that does not collide, or a short ABM account list.
- On Major, attach the Snitchfeed API or MCP connector and start building. Ask an agent to stand up the mention queue app, or start from the five workflows above. The connector talks to Snitchfeed according to its system prompt and scoped access.
If you already have Snitchfeed, skip step one and attach the connector today.