Kelnix Datamind Curator
RemoteAbout Kelnix Datamind Curator
Kelnix Datamind Curator is an API for connecting data sources and preparing structured data and context for AI agents. It combines querying, data cleanup, semantic search, deduplication, PII redaction, and RAG context building.
Key features
- 01Connect and inspect data sources
- 02Query with natural language or SQL
- 03Fetch rows with filters and pagination
- 04Semantic vector search
- 05Standardize, deduplicate, and redact data
- 06Build context and summarize datasets
Use cases
- 01Query connected data for an AI agent
- 02Search indexed data semantically
- 03Clean records and remove duplicates
- 04Prepare context for RAG pipelines
MCP support
Provides MCP clients with tools for managing sources, querying and searching data, cleaning records, redacting PII, and building context. Its MCP endpoint is configured with an API key.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://datamind-api.kelnix.org/stream/mcp/claude mcp add --transport http Kelnix-DataMind-Curator https://datamind-api.kelnix.org/stream/mcp/{"mcpServers":{"Kelnix-DataMind-Curator":{"type":"http","url":"https://datamind-api.kelnix.org/stream/mcp/"}}}Open in Cursor{"servers":{"Kelnix-DataMind-Curator":{"type":"http","url":"https://datamind-api.kelnix.org/stream/mcp/"}}}Open in VS Code{"mcpServers":{"Kelnix-DataMind-Curator":{"type":"http","url":"https://datamind-api.kelnix.org/stream/mcp/"}}}{"mcpServers":{"Kelnix-DataMind-Curator":{"serverUrl":"https://datamind-api.kelnix.org/stream/mcp/"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://datamind-api.kelnix.org/stream/mcp/Auth requiredTools
12| Parameter | Type | Required | Description |
|---|---|---|---|
| source_type | string | Yes | Type of data source to connect. Supported: postgresql, mysql, csv, json_api, mock_crm |
| name | string | Yes | A friendly name for this data source (e.g. 'Production DB', 'Salesforce CRM') |
| connection_string | any | No | Database connection string (e.g. 'postgresql://user:pass@host:5432/db'). Required for database sources. |
| config | any | No | Additional config as key-value pairs (e.g. API URL, headers, file path). Source-type specific. |
Details
From the project README
Kelnix DataMind Curator
🧠 AI-Ready Data & Context Engineering API
Connect any data source, query with natural language, clean and standardize data, build AI-ready context packages, and protect privacy with automated PII redaction.
📖 API Docs • 💰 Pricing • 🌐 Website
One API to unify, clean, and serve your data to AI agents. ⚡
🚀 Quick Start
# 1. Register (free — 25 credits, no credit card)
curl -X POST https://datamind-api.kelnix.org/register \
-H "Content-Type: application/json" \
-d '{"agent_name": "My Agent"}'
# 2. Connect a data source (mock CRM for demo)
curl -X POST https://datamind-api.kelnix.org/sources/connect \
-H "X-API-Key: dm_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"source_type": "mock_crm", "name": "Demo CRM"}'
# 3. Query with natural language
curl -X POST https://datamind-api.kelnix.org/data/query \
-H "X-API-Key: dm_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"source_id": "YOUR_SOURCE_ID", "query": "top 5 companies by revenue"}'
🔌 MCP Integration
Works with Claude Desktop, Cursor, VS Code, and any MCP-compatible client.
Claude Desktop / Cursor
Add to your MCP config:
{
"mcpServers": {
"kelnix-datamind-curator": {
"url": "https://datamind-api.kelnix.org/stream/mcp/",
"headers": {
"X-API-Key": "dm_YOUR_API_KEY"
}
}
}
}
Smithery
npx -y @smithery/cli@latest install @kelnix/kelnix-datamind-curator --client claude
🛠️ Tools
| Tool | Description | Cost |
|---|---|---|
sources.connect | 🔗 Connect a data source (PostgreSQL, MySQL, CRM, API) | 1 credit |
sources.list | 📋 List connected data sources | free |
sources.test | ✅ Test source connectivity | free |
data.query | 💬 Query data with natural language or SQL | 2 credits |
data.fetch | 📥 Fetch raw rows with filters & pagination | 1 credit |
data.search | 🔍 Semantic vector search across indexed data | 2 credits |
pipeline.clean | 🧹 Standardize dates, phones, emails, currencies | 2 credits |
pipeline.deduplicate | 🔄 Remove duplicate records | 1 credit |
pipeline.redact_pii | 🔒 Auto-detect and redact PII | 1 credit |
context.build | 🧠 Build AI-ready context for RAG pipelines | 3 credits |
context.summarize | 📊 AI-powered dataset summary with insights | 2 credits |
credits.check_balance | 💳 Check credits and plan | free |
📦 Supported Data Sources
- 🐘 PostgreSQL — Full SQL support with schema introspection
- 🐬 MySQL — Coming soon
- 🏢 Mock CRM — Built-in demo with companies, contacts, and deals
- 📄 CSV — Coming soon
- 🌐 JSON API — Coming soon
💰 Pricing
| Credits | Price | Per Credit |
|---|---|---|
| 25 | 🎁 Free | on signup |
| 100 | $8 | $0.080 |
| 500 | $30 | $0.060 |
| 1,000 | $50 | $0.050 |
| 5,000 | $200 | $0.040 |
| 10,000 | $400 | $0.040 |
Monthly plans: Basic (200/mo, $15) · Pro (2,000/mo, $99)
💳 Pay with Stripe (cards) or 🪙 300+ cryptocurrencies.
📖 API Docs
Interactive Swagger docs: datamind-api.kelnix.org/docs
🏗️ Self-Hosting
git clone https://github.com/kelnixsolutions/Kelnix-DataMind-Curator.git
cd Kelnix-DataMind-Curator
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # edit with your keys
uvicorn app:app --host 0.0.0.0 --port 8001
🏛️ Architecture
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ MCP Clients │ │ REST API │ │ Registries │
│ Claude/Cursor│────▶│ FastAPI │◀────│ Smithery/etc │
└──────────────┘ └──────┬───────┘ └──────────────┘
│
┌──────────┼──────────┐
│ │ │
┌──────▼──┐ ┌────▼────┐ ┌───▼──────┐
│Connectors│ │Pipeline │ │ Context │
│ PG/CRM │ │Clean/PII│ │Build/NLQ │
└─────────┘ └─────────┘ └──────────┘
│ │ │
┌──────▼──┐ ┌────▼────┐ ┌───▼──────┐
│ SQLite │ │ Redis │ │ ChromaDB │
│ Billing │ │ Cache │ │ Vectors │
└─────────┘ └─────────┘ └──────────┘
📜 License
MIT — Kelnix Solutions
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.