Data · Remote MCP server
Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract
Run one LLM instruction over every row to classify, extract or summarise into new columns.
Visit Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract's website
What the MCP Registry states
The entry as published to the official MCP Registry (read 2026-10-04), latest version.
- Registry name
io.github.Nero-Engine/dataset-ai-enrich- Version
- 0.1.0
- Status
- Active
- Category
- data
- Transport
- Streamable HTTP
- Published
- 2026-09-13
- Updated
- 2026-09-13
- Publisher
- Nero-Engine (GitHub) · 43 servers with pages here
- Website
- apify.com/nerolabs/dataset-ai-enrich
- Source
- Registry API entry
Remote endpoints
| Transport | URL | Headers declared |
|---|---|---|
| Streamable HTTP | https://mcp.apify.com/?tools=nerolabs/dataset-ai-enrich | Authorization (required, secret) |
How to connect Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract
Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract is a remote MCP server: there is nothing to install. Its endpoint is https://mcp.apify.com/?tools=nerolabs/dataset-ai-enrich, served over Streamable HTTP. In an assistant that accepts remote MCP servers (often under a setting named connectors, integrations or tools), add a new server and give it this URL; in a client configured by file, add it as a remote (HTTP) server with the same URL.
The registry entry declares an HTTP header for the connection: Authorization (required, secret). A secret header usually carries an API key or token issued by the service; clients that accept custom headers set it with the server's URL.
Derived from the registry entry, not tested here. What the server does, and on what terms, is set by its publisher; check its repository or website before giving it access to your accounts or files. How to add an MCP server to an assistant · Before you connect
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