Syntitan

Created By

CUBIGa month ago

Syntitan MCP server — connect Claude and other MCP clients to Syntitan, the AI-Ready Data Platform that fills the missing layer between enterprise data management and AI execution.

# mcp

# mcp-server

Overview Content Tools Comments

Overview

Syntitan MCP Server

The official Model Context Protocol (MCP) server for Syntitan by CUBIG — the AI-Ready Data Platform that fills the missing layer between enterprise data management and AI execution. It lets Claude and other MCP-compatible clients (Cursor, VS Code, Claude Code, ChatGPT, etc.) securely connect to Syntitan through natural language.

Category: Data & Analytics
Transport: Streamable HTTP
Authentication: OAuth 2.0
Endpoint: https://mcp.syntitan.ai/mcp

Connecting

Add Syntitan as a remote MCP server in your client using the endpoint URL below:

https://mcp.syntitan.ai/mcp

On first connection your browser opens the Syntitan sign-in page. Log in and authorize access; the client then connects automatically. No API keys are stored in the client.

Cursor, VS Code, Claude Code, and other MCP clients support remote MCP servers — add the endpoint URL above following each client's connector instructions.

Tools

Tool (title)	Description
Estimate Refinement Credits	Preview the estimated credits and time required before running a refinement
Get AI Readiness Detail	Break down a specific AI Readiness category to show which columns contributed and by how much
Get AI Readiness Summary	Return a dataset's overall AI Readiness score with per-category scores (Privacy, Conciseness, …) and the top-priority issues
Get Column Profile	Return detailed statistics for a single column, such as distinct/top values, missing rate, and example values
Get Dataset History	Return a dataset's full version history, including each version's release status, author, timestamp, and changes (masked PII entities and applied refinement modules)
Get Refinement Options	Return the available preprocessing options for a dataset, such as preprocessing modules and de-identification targets
Get Refinement Status	Return the progress and status of a running or completed refinement
Get Dataset Sample Rows	Return a sample of rows from a dataset with PII columns auto-masked; refuses if unmasked PII is present
Get Dataset Schema	Return a dataset's column structure — type, missing values, PII flags — without exposing PII column values
Get Diagnosis Guide	Return guidance on how to interpret AI Readiness diagnostic metrics and scores
List Datasets	List datasets the user can access, with each one's latest version, row/column counts, AI Readiness score, and last-modified time
Start Refinement	Run preprocessing/de-identification on a dataset (consumes credits); results are saved as a new snapshot

Example use cases

"What datasets do I have right now?" — Lists your datasets with their latest version, row/column counts, AI Readiness score, and last-modified time.
"Give me a full diagnosis of this dataset." — Returns the overall AI Readiness score with a per-category breakdown (Privacy, Conciseness, Contextuality, …) and the highest-priority issues to fix first.
"Which columns have the missing values, and why?" — Drills into a specific category to show exactly which columns contributed and by how much.
"Show me 10 sample rows where region is null." — Returns sampled rows; PII columns are returned only in masked form, and the request is refused if the dataset still has unmasked PII.
"Mask all the PII columns in this dataset." — Runs Syntitan's de-identification preprocessing in place and reports the before/after AI Readiness change.

Authentication & security

Authentication uses OAuth 2.0. Users sign in through the Syntitan website; credentials are never shared with the MCP client. All traffic is served over HTTPS/TLS.

Data handling

Syntitan accesses only the data required to fulfill a user's request. Audit logs (which may include user identifiers such as email) are retained for operational and security purposes in accordance with our privacy policy. Data collection, storage, retention, and deletion practices are described in the privacy policy linked below.