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Real-time AI news, status, model pricing, and pay-per-call premium tools (USDC on Base).
Real-time AI news, status, model pricing, and pay-per-call premium tools (USDC on Base).
This MCP server for TensorFeed.ai provides AI news, model data, and premium endpoints with a payment system. While authentication and authorization are present for premium features, there are moderate security concerns: the code is incomplete/truncated making full analysis impossible, sensitive payment logic and wallet addresses are handled in production code, error handling could be more robust, and input validation relies on manual parsing rather than schema validation for many endpoints. Permissions are appropriate for the service's purpose (network access for APIs, KV storage for caching). Supply chain analysis found 10 known vulnerabilities in dependencies (0 critical, 2 high severity). Package verification found 1 issue (1 critical, 0 high severity).
4 files analyzed · 21 issues found
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Set these up before or after installing:
Environment variable: TENSORFEED_TOKEN
Add this to your MCP configuration file:
{
"mcpServers": {
"ai-tensorfeed-mcp-server": {
"env": {
"TENSORFEED_TOKEN": "your-tensorfeed-token-here"
},
"args": [
"-y",
"tensorfeed"
],
"command": "npx"
}
}
}From the project's GitHub README.
This is a Next.js project bootstrapped with create-next-app.
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
Open http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.
This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.
To learn more about Next.js, take a look at the following resources:
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details.
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