How long does the integration take?

Change one base URL in an OpenAI-compatible client, or install the Python or TypeScript SDK; the quickstart routes a first request in a few minutes.

Will this lock us into another vendor?

No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.

What happens when a provider fails?

The router falls back to the next model your policy allows and records the fallback in the trace, so an outage costs a retry, not an incident.

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OpenSmartRoute

Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract - Tool - OpenSmartRoute

Toolv0.1.0

Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract

Name: Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract
Author: io.github.Nero-Engine

Run one LLM instruction over every row to classify, extract or summarise into new columns.

by io.github.Nero-Engine(0) 0 installs

Free

Free account. Installing gives you the manifest plus copy-paste snippets.

See reviews

About

Imported from the official MCP registry - entry io.github.Nero-Engine/dataset-ai-enrich v0.1.0. Ratings and installs below are from this marketplace.

Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract

Run one LLM instruction over every row to classify, extract or summarise into new columns.

Remote endpoints

streamable-http https://mcp.apify.com/?tools=nerolabs/dataset-ai-enrich

Registry entry: io.github.Nero-Engine/dataset-ai-enrich

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

# after Install: the listing is in your workspace's routing pool - nothing else to configure
curl -s -X POST https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'
# or pin it on the OpenAI-compatible endpoint: {"model": "mcp-io-github-nero-engine-dataset-ai-enrich", ...}

Installed into a catalogue, chosen by a router

Install Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

Photo: RDNE Stock project

For the business

What it costs to adopt - in engineering time.

Same client, same code
One base URL
The OpenAI-compatible endpoint answers your existing SDK calls; model=auto lets the router choose.
The SDKs
One URL, every vendor
No vendor lock-in
Point your OpenAI-compatible client at one address; add, swap or retire providers in the catalogue without touching application code.
Every vendor side by side
Next eligible model answers
Failover built in
When a provider is down or over quota the request moves on; the trace says it did.

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

mcp-io-github-nero-engine-dataset-ai-enrich.ocm.jsonjson

{
  "ocm": "1",
  "id": "mcp-io-github-nero-engine-dataset-ai-enrich",
  "kind": "tool",
  "name": "Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract",
  "description": "Run one LLM instruction over every row to classify, extract or summarise into new columns.",
  "publisher": "io.github.Nero-Engine",
  "version": "0.1.0",
  "capabilities": {
    "domains": [
      "data_analysis"
    ],
    "tags": [
      "mcp",
      "mcp-server",
      "mcp-registry"
    ],
    "languages": [
      "en"
    ],
    "supports_tools": true
  },
  "quality_prior": 0.6,
  "examples": [
    "Run one LLM instruction over every row to classify, extract or summarise into new columns."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "mcp-registry",
      "path": "server.json",
      "ref": "0.1.0",
      "url": "https://registry.modelcontextprotocol.io/v0/servers/io.github.Nero-Engine%2Fdataset-ai-enrich",
      "key": "io.github.Nero-Engine/dataset-ai-enrich",
      "registry": "https://registry.modelcontextprotocol.io"
    }
  },
  "endpoints": [
    {
      "protocol": "mcp",
      "url": "https://mcp.apify.com/?tools=nerolabs/dataset-ai-enrich"
    }
  ]
}

Fetch it by URL: GET /api/v1/registry/mcp-io-github-nero-engine-dataset-ai-enrich/manifest?version=0.1.0

About

Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract

Remote endpoints

Use it

Install Dataset AI Enrichment: Bulk LLM & GPT Classify and Extract and it becomes one more candidate the router can pick - when it fits.

What it costs to adopt - in engineering time.

One base URL

No vendor lock-in

Failover built in

Manifest

Reviews