Analytics

painspotter.ai

Identifies market opportunities and trends by analyzing social signals and community evidence.

1 endpoint7 known toolsFirst detected July 25, 2026Last detected August 29, 2026

ENDPOINT 1

https://painspotter.ai/mcp

No auth detected

MCP server metadata

Name: PainSpotter
Version: 2.14.7

Capabilities

experimentalprompts.listChangedresources.listChangedtools.listChangedtasks.listtasks.canceltasks.requests.tools.calltasks.requests.prompts.gettasks.requests.resources.read

Server instructions

PainSpotter is a database of AI-analyzed business opportunities mined from Reddit, Hacker News and Product Hunt discussions. Data is layered: Category (domain) > Theme (a clustered pain point with trend signal) > Opportunity (a concrete product idea with commercial scores). Suggested flow: call get_overview first to learn the landscape, then list_trending_themes or query_opportunities to drill in, get_theme for market-signal context, and get_opportunity for full detail. For long-form weekly analyses, use list_blog_posts and get_blog_post (free, no quota cost). Tiering: get_overview, get_opportunity, list_blog_posts and get_blog_post are free. query_opportunities, list_trending_themes and get_theme require a Pro plan. When a tool returns an upgrade notice, relay it to the user and point them to https://painspotter.ai/pricing.

Known tools 7

get_overview

Get a global overview of PainSpotter: all domain categories (with theme count, opportunity count and 30-day mentions) plus a snapshot of currently trending themes.

Inferred read-only

get_opportunity

Get the full detail of a specific opportunity: description, score breakdown, MVP features, competitors, differentiation, risks and community evidence count.

Inferred read-only

query_opportunities

Unified opportunity search: filter by keyword, minimum score, platform, recommendation tier and domain category, then sort the results.

Inferred read-only

list_trending_themes

List themes that are currently heating up (sorted by trend change %), to catch demand that is growing.

Inferred read-only

get_theme

Get the full market signal for a single theme: trend, 30-day / all-time mentions, signal channels, audience clarity and market summary, plus the top-scoring opportunities under it (including the Best Bet flagship opportunity).

Inferred read-only

list_blog_posts

List recent published PainSpotter blog posts — weekly long-form analyses of validated business opportunities (who's hurting, why now, how to build it).

Inferred read-only

get_blog_post

Get the full Markdown body of one published blog post by its slug.

Potential side effects

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.painspotter]
url = "https://painspotter.ai/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "painspotter": {
      "type": "http",
      "url": "https://painspotter.ai/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: painspotter
Remote MCP URL: https://painspotter.ai/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "painspotter": {
      "url": "https://painspotter.ai/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "painspotter": {
      "type": "http",
      "url": "https://painspotter.ai/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "painspotter",
  "transport": "streamable-http",
  "url": "https://painspotter.ai/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

TRUST AND VERIFICATION EVIDENCE

Loading Trust v2 evidence…

Checking the associated registrable domain. The BuiltWith key remains server-side.

Indexed

Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.