Syntitan
About
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.
Details
- Transport
- SSE
Explore
- Eleven tools for data exploration, profiling, refinement, and AI readiness diagnosis.
- Authenticates via OAuth 2.0; credentials never shared with the MCP client.
- All traffic served over HTTPS/TLS.
- PII columns are auto-masked in sample rows and column profiles.
- Refinement consumes credits and saves results as a new snapshot.
- Supports Cursor, VS Code, Claude Code, and other MCP clients.
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.
list_datasets
List datasets the user can access, with each one's latest version, row/column counts, AI Readiness score, and last-modified time. Timestamps are ISO 8601 in UTC; when presenting, infer the user's timezone from conversation context and convert, otherwise show UTC. Results are paginated (`total` = full count).
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). Timestamps are ISO 8601 in UTC; infer the user's timezone from context and convert, otherwise use UTC.
get_ai_readiness_summary
Return a dataset's overall AI Readiness score with per-category scores (Integrity, Contextuality, Conciseness) and the top-priority issues. For metric definitions and interpretation, call `get_diagnosis_guide`.
get_ai_readiness_detail
Break down a specific AI Readiness category to show which columns contributed and by how much. category: 'integrity' (nulls/outliers/type-mismatches), 'contextuality' (semantic coverage), 'conciseness' (unique values/redundancy). For metric definitions, call `get_diagnosis_guide`.
get_dataset_schema
Return a dataset's column structure — type and missing values — without exposing PII column values. For metric definitions and interpretation, call `get_diagnosis_guide`.
get_column_profile
Return detailed statistics for a single column, such as distinct/top values, missing rate, and example values. Returns statistics only (no raw values). For metric definitions and interpretation, call `get_diagnosis_guide`.
get_dataset_sample_rows
Return a sample of rows from a dataset with PII columns auto-masked; refuses if unmasked PII is present. filters: list of {column, op, value}; op one of eq|ne|gt|gte|lt|lte|is_null|not_null|contains (AND). Returns up to 50 rows plus total_matched. Unmasked PII columns are excluded (listed in `excluded_columns`); filtering on a PII column is rejected.
get_diagnosis_guide
Return guidance on how to interpret AI Readiness diagnostic metrics and scores. Call this before interpreting diagnosis results to avoid common misreadings. No arguments.
get_dataset_run_options
Return the available preprocessing options for a dataset, such as preprocessing modules and de-identification targets. Includes recommended/disabled flags and detected PII entity types eligible for redaction (redaction: {entities: [detected uppercase entity keys], available: bool}). Call this first before estimate_dataset_run or start_dataset_run to discover valid parameter values.
estimate_dataset_run
Preview the estimated credits and time required before running a refinement. Call get_dataset_run_options first to see valid refinement_modules and redaction_entities values, then start_dataset_run to execute.
start_dataset_run
Run preprocessing/de-identification on a dataset (consumes credits); results are saved as a new snapshot. Call get_dataset_run_options first to discover valid module and entity values, then estimate_dataset_run to preview cost. Because this charges credits, ALWAYS show the user the estimated cost and the modules/entities to be applied, then get their explicit confirmation before calling (a natural confirmation in the conversation, not a rigid Y/n prompt). Returns a run_id; poll get_dataset_ru…
get_dataset_run_status
Return the progress and status of a running or completed refinement. Use the run_id returned by start_dataset_run; poll until status is 'completed' or 'failed'. On completion, returns before/after AI Readiness scores and trust breakdown (integrity, contextuality, conciseness).
| 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 |
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
1. "What datasets do I have right now?" — Lists your datasets with their latest version, row/column counts, AI Readiness score, and last-modified time. 2. "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. 3. "Which columns have the missing values, and why?" — Drills into a specific category to show exactly which columns contributed and by how much. 4. "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. 5. "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.Privacy & terms
- Privacy Policy: https://syntitan.ai/privacy-policy - Terms of Service: https://syntitan.ai/terms-of-serviceSupport
- Email: [email protected] - Company: CUBIG Corp.License
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