contact-dedupe
Related Servers
Alternatives to contact-dedupe
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityCmaintenanceEnables converting PDFs and CSV/spreadsheet files into spreadsheets, extracting bank statement or receipt data into clean CSV, cleaning messy CSV, and generating client-ready reports through an open MCP endpoint.-
- AlicenseNot gradedqualityBmaintenanceEnables cleaning clinical-research tables by mapping fields, classifying and normalizing cells, routing confidence-gated decisions to a review queue, and exporting reports and audit logs through an MCP stdio interface.MIT
- AlicenseNot gradedqualityCmaintenanceEnables detection of duplicate patient records for a Master Patient Index by scoring record pairs and providing verdicts and reasons, intended for synthetic data only.MIT
- FlicenseNot gradedqualityBmaintenanceEnables reading, normalizing, validating, merging, and exporting data from Excel, CSV, JSON, and SQLite sources into a unified schema, with tools exposed via FastMCP.1-
- FlicenseNot gradedqualityBmaintenanceEnables MCP-compatible AI clients to validate healthcare claims data quality by running completeness, integrity, and temporal checks on CSV files via five callable tools, including profiling and full scans.-
- AlicenseBqualityDmaintenanceComprehensive CSV processing MCP server with 40+ operations for data manipulation, analysis, and validation. Features auto-save, undo/redo, and handles GB+ files3925MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct role: profiling the CSV, finding duplicate groups, performing the deduplication, and comparing individual pairs. No overlap in purpose or output.
All tool names follow a consistent verb_noun pattern in snake_case: profile_csv, find_duplicates, dedupe_csv, compare_records. The style is uniform and predictable.
Four tools is exactly right for a focused deduplication workflow. Each tool serves a distinct step in the process without unnecessary bloat or missing core functionality.
The workflow is complete: profile to understand data, identify duplicates, execute deduplication, and fine-tune via pair scoring. No obvious dead ends or required operations that agents cannot perform.