Kelnix Datamind Curator

Connect multiple data sources to query, clean, and standardize information for seamless integration into your workflows.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

  • Sources.Connect: Connect a new data source for querying, fetching, and context building. Validates connectivity before saving. Use 'mock_crm' for a built-in demo with sample companies, contacts, and d
  • Sources.List: List all connected data sources with their status and type. Returns source_id, name, type, status, and creation date for each source. Free — no credits consumed.
  • Sources.Test: Test connectivity to a data source and list available tables. Returns connection status, database version info, and up to 20 table names. Free — no credits consumed.

What data it sees

Do you need an account

No: the server works without sign-in

Connect multiple data sources to query, clean, and standardize information for seamless integration into your workflows. Enhance your retrieval-augmented generation pipelines by building structured context packages and performing semantic vector searches across indexed records. Protect sensitive data through automated PII redaction while streamlining data exploration using either natural language or SQL queries.

How to connect

How to connect Kelnix Datamind Curator to Claude

The server address to paste into Claude settings:

https://kelnix-datamind-curator--kelnix.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Kelnix Datamind Curator to ChatGPT

The server address to paste into ChatGPT settings:

https://kelnix-datamind-curator--kelnix.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Kelnix Datamind Curator to Cursor

The server address to paste into Cursor settings:

https://kelnix-datamind-curator--kelnix.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (12)

Raw names from tools/list. Only developers need these.

sources.connectConnect a new data source for querying, fetching, and context building. Validates connectivity before saving. Use 'mock_crm' for a built-in demo with sample companies, contacts, and deals data. Costs 1 credit.
sources.listList all connected data sources with their status and type. Returns source_id, name, type, status, and creation date for each source. Free — no credits consumed.
sources.testTest connectivity to a data source and list available tables. Returns connection status, database version info, and up to 20 table names. Free — no credits consumed.
data.queryQuery data using natural language or SQL. In 'auto' mode, the system detects whether your query is plain English or SQL. Natural language queries are translated to SQL by Claude Haiku using your schema. Returns columns, rows, the executed SQL, and the mode used. Costs 2 credits.
data.fetchFetch raw rows from a specific table with optional filters and pagination. Returns columns, rows, row count, and total available records. Costs 1 credit.
data.searchSemantic vector search across your indexed data using ChromaDB embeddings. Finds records that are semantically similar to your natural language query, even if they don't contain the exact keywords. Data must be indexed first (happens automatically when you use context.build or data.fetch). Costs 2 credits.
pipeline.cleanClean and standardize data formats — dates to ISO 8601, phones to digits, emails to lowercase, currencies to floats. Auto-detects field types from names and values when rules are not provided. Fills nulls with sensible defaults. Returns cleaned data with transformation stats. Costs 2 credits.
pipeline.deduplicateRemove duplicate records based on specified key fields or full record hash. When keys are provided, two records are considered duplicates if they have identical values for all specified key fields. Returns deduplicated data with stats. Costs 1 credit.
pipeline.redact_piiDetect and redact personally identifiable information (PII) from records. Auto-detects emails, phone numbers, SSNs, credit cards, IP addresses, names, and addresses using field name heuristics and regex patterns. Returns redacted data with stats on what was found and removed. Costs 1 credit.
context.buildBuild an AI-ready context package from one or more data sources. Fetches schemas, sample data, and relevant records from each source, then assembles them into structured chunks optimized for LLM consumption. Perfect for RAG pipelines. Also indexes data for semantic search. Costs 3 credits.
context.summarizeGenerate an AI-powered summary of a dataset with key insights. Uses Claude Haiku to analyze the data and produce a narrative summary, key insights as bullet points, and data quality observations. Costs 2 credits.
credits.check_balanceCheck your current credit balance and subscription plan. Returns remaining credits and active plan (free, basic, or pro). Call this before expensive operations to verify you have enough credits. Free — no credits consumed.