"System capable of integrating with ERP and other information system databases" matching MCP servers:

Exposes ERP data (production orders, inventory, BOM, customers) to any MCP client through a backend-neutral interface, with a mock backend for testing.

Enables AI assistants to query and operate Dingjie ERP system via natural language, covering procurement, sales, and material management.

Enables LLMs and agents to interact with relational databases (SQL Server, MySQL, PostgreSQL) through MCP tools. Supports executing queries, inserting records, listing tables, and exposing database schemas with secure credential management.

Material, cell, pack, and supply-chain management with real-time

Enables AI agents to query and operate on ERP data through a unified tool interface, working across CSV exports, SFTP drop folders, SQLite/ODBC, and optional enterprise APIs. It supports purchase orders, vendors, and inventory lookups while keeping agent-facing tools consistent regardless of backend.

Enables AI to interact with Microsoft Access (.mdb, .accdb) and SQLite 3 databases, including read-only support for Access 97 databases. Supports executing SQL queries, importing/export data from CSV and Excel files, and storing notes about database files.

Enables AI agents and LLM clients to securely query and write PI time-series archives, AF asset databases, elements, attributes, and summaries through standard tool calls, with optional off-by-default tools for building and maintaining AF structures and dry-run modifications.

It enables LLMs to query enterprise data such as sales orders, stock levels, and vendor information through structured MCP-compliant tools, using localized JSON databases for demonstration.

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

Enables querying ERP data via an external API with adaptive caching, and accessing data from Google Sheets or local Excel/CSV files through dual MCP endpoints.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables users to convert a vendor partner directory (such as PartnerPage or the Shopify Partner Directory) or a trade body member list into one company row per listing, each carrying website, domain, tier, tags, and location for Clay-ready output. It runs the extraction on Apify, returning dataset rows for up to 20 directory URLs with optional deduping, detail enrichment, and a cost cap.

Enables agent tools to validate queries, assess token costs, and extract structured datasets from unstructured text using multiple LLM providers.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Enables AI to interact with internal company systems through API integration, supporting data search, information updates, report generation, and system connectivity.

Exposes local DuckDB pizza sales data as MCP tools for querying store-level and total sales metrics via natural language, enabling local LLMs to perform real-time analytical queries.

Provides stock market data and computes technical indicators like EMA, MACD, and RSI across multiple timeframes using the LongPort API and TA-Lib. It enables users to perform detailed financial analysis and multi-timeframe market reviews through standard MCP tools.

Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.

It enables AI assistants to interact with StarRocks databases by running SQL queries, exploring schemas and data, accessing system information, and generating Plotly charts from query results.

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.