"Replicating ChatGPT Memory Functionality for Task Management and Skill Tracking" matching MCP servers:

MCP server for creating, listing, inspecting, and validating tracking plans for ecommerce and lead-generation businesses, with JSON storage and TypeScript implementation.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

Enables AI assistants to inspect and extract data from local CSV, TSV, JSONL and log files far larger than available RAM, using bounded memory, crash-resilient resumable runs, and streamed progress. All file access is confined to user-approved directories, keeping the data private to the machine.

A fast, safe MCP server for YNAB providing read-only budget access by default, with 28 tools for budgets, transactions, categories, payees, accounts, scheduled transactions, and server-computed analytics.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

Enables LLMs to interact with any ERPNext instance through comprehensive CRUD operations, advanced permissions, and a web chat interface.

Enables AI agents to interact with the WYGIWYH expense tracking API through 75 dynamically generated MCP tools. Supports comprehensive financial operations including transaction management, account handling, recurring expenses, and investment tracking.

Enables natural language-powered ETL workflows using Airflow, AWS Glue, Athena, and S3, allowing LLM agents to control and monitor data infrastructure.

Enables programmatic management of a Dify instance, including datasets, apps, and tools.

Read-only MCP tools for TROCCO API, enabling workflow and BigQuery datamart audit information retrieval.

Enables deterministic reconstruction of complex tables from OCR bounding boxes, including multi-column layouts, merged headers, grid hierarchies, formula auditing, and PII redaction through MCP.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

Audits extracted balance sheets, income statements, and cash flow reports for mathematical consistency and formula balancing, reconstructing complex financial tables and resolving cross-references from document or OCR payloads. Also performs spatial layout parsing and PII redaction, returning structured matrices and validation telemetry over MCP.

Enables ChatGPT users to view and inspect nodes, jobs, executions, recent errors, and bounded job logs in linked workspaces through read-only MCP tools, including sidebar views and mention-based queries.

Enables deterministic reconstruction of complex tables from OCR bounding-box payloads, recovering multi-column layouts, merged headers, and grid hierarchies. It also supports formula consistency auditing and sensitive PII redaction for document processing pipelines.

An MCP server for controlling Apache Airflow 3 via its REST API, enabling operations like DAG management, task monitoring, and more through natural language.

Enables agents to synchronize, merge, and reconcile distributed state across nodes using conflict-free last-write-wins element sets with Lamport/vector-clock causality, token-bucket gossip dissemination, and quorum-based commit coordination. It runs entirely on the Python standard library and exposes these primitives to Claude Desktop, Cursor, and Windsurf over the MCP JSON-RPC stdio protocol.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Enables AI agents and MCP clients to heuristically parse multi-column document dumps, aligned spacing, and CSV/TSV input, converting them into clean GitHub Flavored Markdown tables and typed JSON. Runs fully offline on the Python standard library with zero external dependencies, returning deterministic results without LLM round-trips.