PostHog MCP Server
Official PostHog MCP Server for product analytics, feature flags, experiments, and more.
Test ads, prices and sales pitches on simulated buyers; get a go / iterate / no-go report.
Pick the AI app you use, then copy the settings.
Passed 3/3 checks · Runs on your computer · ✓ Commands checked Nov 13, 2026
First: which AI do you use?
Pick your app and the steps appear below.
Works with Claude, with some setup first
Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
One time only; it works for every server that needs Python (uv)
SettingsDeveloperEdit Config
{
"mcpServers": {
"market-mirror": {
"command": "uvx",
"args": [
"marketmirror"
]
}
}
}Use MarketMirror and tell me what you can do for me, then show me one thing✓ If Claude says it is using MarketMirror or shows a MarketMirror tool in the answer, you are connected
Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version
ChatGPT cannot connect MarketMirror yet
MarketMirror runs on your computer, but ChatGPT can only connect servers that run on the web
A · Use an app that can connect MarketMirror
B · Others in Marketing & social media that work with ChatGPT
Picked from the most used in the same category, only ones that connect by address with no key
Works with Claude Code, with some setup first
Install Python (uv) on your computer first (one time)
One time only; it works for every server that needs Python (uv)
Mac: press Command+Space and type Terminal · Windows: open PowerShell
claude mcp add --scope user market-mirror -- uvx marketmirrorYou should see MarketMirror listed as connected
Use MarketMirror and tell me what you can do for me, then show me one thing✓ If Claude Code says it is using MarketMirror or shows a MarketMirror tool in the answer, you are connected
Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Works with Cursor, with some setup first
Install Python (uv) on your computer first (one time)
One time only; it works for every server that needs Python (uv)
Use MarketMirror and tell me what you can do for me, then show me one thing✓ If Cursor says it is using MarketMirror or shows a MarketMirror tool in the answer, you are connected
Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Works with VS Code, with some setup first
Install Python (uv) on your computer first (one time)
One time only; it works for every server that needs Python (uv)
Use MarketMirror and tell me what you can do for me, then show me one thing✓ If VS Code says it is using MarketMirror or shows a MarketMirror tool in the answer, you are connected
Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Claude Code
claude mcp add --scope user market-mirror -- uvx marketmirrorClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"market-mirror": {
"command": "uvx",
"args": [
"marketmirror"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"market-mirror": {
"command": "uvx",
"args": [
"marketmirror"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"market-mirror": {
"type": "stdio",
"command": "uvx",
"args": [
"marketmirror"
]
}
}
}These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
pypi: marketmirrorBased on how this server connects and on each app’s own docs, checked Nov 10, 2026.
Run on your computer in
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 14, 2026
The registry name was verified with the GitHub account DarkWorldCoder, which owns the repo
Code last changed 2 days ago
Uses AGPL-3.0
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Ask, share a tip or report that it does not work. Sorted by what people marked helpful.
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Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed 2 days ago, and the latest version (0.4.2) reached the registry 19 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: PostHog MCP Server, Openshorts, Google Ads + Meta Ads + SEO/GEO MCP
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Official PostHog MCP Server for product analytics, feature flags, experiments, and more.
Turn long videos into viral vertical shorts and publish them to TikTok, Instagram and YouTube.
Google Ads, Meta Ads, Google Analytics (GA4) & Search Console MCP server by Ryze AI
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Try your ads, prices, and sales pitch with simulated buyers before spending on a real campaign.
English · 简体中文
Open source and built for AI agents. Interviewed LLM buyer personas scroll simulated ad feeds, argue in an OASIS social simulation, answer a survey and hear your sales pitch. Ask in plain words (*"test these three ads on young parents in India"*) from Claude Code, Codex, Gemini CLI, Cursor or any MCP client, and get a go / iterate / no-go report in one offline HTML dashboard. Self-hosted: only the model calls (to the provider you choose) and optional public research leave your computer.
Built on MiroFish and CAMEL-AI's OASIS · listed in the official MCP Registry as io.github.DarkWorldCoder/market-mirror
[Open the live demo](https://darkworldcoder.github.io/market-mirror/demo/): the full dashboard from a 2,999-buyer simulated run, in your browser, no install. (Fitloop is an invented app; every number is simulated.)
Try it (Linux, macOS or WSL; needs Git, uv and Python 3.12):
git clone https://github.com/DarkWorldCoder/market-mirror.git && cd market-mirror
bash install.sh --configure # installs the engine, then asks which model provider to use ./mm start && ./mm cli quickstart --out scenarios/acme # 9 questions; prepares a small simulated market, then stops for review
The last line already makes model calls on the provider you chose, so start with a small market. If you chose a Claude Code or Codex login, run ./mm gateway start claude (or codex) before it. Already in an MCP client? Add the server with claude mcp add market-mirror -- uvx marketmirror (any client: command uvx, args ["marketmirror"]), then ask your agent to run doctor and setup. Full quick start.
If MarketMirror saves you one bad launch, a ⭐ helps other people find it. To hear about new tests and releases, use Watch → Custom → Releases.
▶ Watch the 60-second launch film (with captions) · vertical 9:16 cut · made with HyperFrames from launch-video/
Meet the buyers · What you can test · Screenshots · How it works · Example results · Evidence and limits · Get started · Documentation
MarketMirror gives you a practice audience for your next marketing decision. Describe your product and the people you want to reach. It creates AI-generated people who can react to your ads, compare prices, ask questions, raise concerns, and respond to a sales pitch.
You get a report explaining what interested them, what put them off, what to change, and what to test with real customers next. You can also watch the simulation and explore the results in a dashboard.
For example: you are launching a fitness app. Should the ad lead with “15 minutes a day” or “less than one gym visit”? Should the free trial ask for a payment card? MarketMirror lets you rehearse those choices before a live campaign.
The buyers are simulated. Their responses are generated by AI, so use them to develop and compare ideas. Confirm promising findings with real customers and small live experiments.
| If you are… | Use MarketMirror to… |
|---|---|
| A founder or small business owner | Explore how different people might understand your product, price, or launch offer. |
| A marketer or agency | Compare ads, headlines, emails, and audiences before choosing a live test. |
| A sales team | Practice a pitch, discover likely concerns, and try clearer answers. |
| A product or research team | Explore buying decisions and website journeys, then plan customer research. |
| A developer or AI agent builder | Run the same workflows through commands, an API, a Python library, or connected AI assistants. |
A simulated buyer is an AI-generated character representing someone in your intended audience. The character has a background and practical reasons for accepting, questioning, or rejecting an offer.
You choose the groups you want to explore—for example, students, working parents, and young professionals in selected countries. MarketMirror creates different people within those groups and can ask them about their lives before showing them your marketing.
| What a buyer can have | Why it matters in a test |
|---|---|
| A background | Age, location, work, and life stage provide context for their response. |
| A budget and priorities | An offer may interest them but still compete with more urgent expenses. |
| A phone and internet connection | A heavy page or video may be inconvenient on their device or data plan. |
| People they consult | A partner, family member, or community may influence a purchase. |
| Trust concerns | Hidden fees, unclear claims, or a previous bad experience may make them hesitate. |
| A buying decision card | A summary records what could persuade them, what could stop them, and who else matters. |
An illustrative buyer: a busy parent who wants short home workouts, uses a budget phone, and worries about surprise charges. They might like a low monthly price but reject a “free” trial that immediately asks for a payment card. Another buyer may care more about workout quality than price. These are invented examples, not findings from real customers.
In tests within the same run, buyers can remember earlier experiences. Someone who disliked an ad can carry that reaction into a later survey or pitch. Separate comparison scenarios keep their own histories.
Detailed characters help you explore different viewpoints. They do not make the generated answers representative of a real population.
| Your question | What MarketMirror does | What you can learn from the simulation |
|---|---|---|
| Which ad gets attention? | Places your ads among ordinary posts in simulated Facebook, Instagram, Reddit, TikTok, LinkedIn, or X feeds. | Which ads buyers stop at, ignore, click, comment on, or hide. |
| Which headline or message is clearer? | Shows different versions to different buyers and asks for their reactions. | Which wording draws interest and which creates confusion. |
| Does the price or offer feel right? | Asks price questions and compares offers or pricing tiers. | Where price becomes a concern and which terms need explaining. |
| What might happen around a launch? | Lets buyers post, reply, and react in a simulated social conversation with scheduled brand posts and events. | Questions, objections, and themes that emerge in the discussion. |
| Will an email interest them? | Tests subject lines, email content, or a sequence in simulated inboxes. | Generated opening, reading, and response choices. No emails are sent. |
| How does our sales pitch land? | Presents the pitch, collects concerns, tries your answers, and asks again. | Which concerns persist and whether your response helps or makes things worse. |
| Where does our website lose people? | Sends simulated visitors through a real website and records their actions. | Where they hesitate, leave, or complete a checked task such as adding to cart. |
| Which audience should we investigate first? | Compares responses across the groups and countries you selected. | Differences worth following up with real customer research. |
| What should we test next? | Uses the run and, when supplied, real campaign exports to suggest follow-up experiments. | A practical starting point for the next live test. |
| Do the simulations match our results? | Compares paired simulated and observed outcomes. | How well the simulation held up on those experiments and where it needs adjustment. |
You can repeat a comparison to see whether its result changes between runs. You can also use different AI providers and inspect where they disagree. Both help reveal uncertainty within the simulation.