"Understanding Frontend and Backend Development" matching MCP servers:

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.

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.

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.

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.

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.

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

MCP server for FormsList, a form-backend SaaS. Enables creating form endpoints, reading submissions, configuring auto-responses, and generating embed snippets via natural language.

Enables coding agents to interact with Metaflow workflows, including querying flows, runs, tasks, logs, and artifacts across any Metaflow backend.

Enables creating and inspecting Seed tables and relationships through the Seed backend HTTP API.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.

Enables large language models to retrieve up-to-date and correct information about dlt pipelines, datasets, and schemas, improving AI-assisted development.

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

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

Enables data engineers and BI professionals to perform data pipeline development, quality assurance, visualization, and API integration locally with AI assistance.

Turns existing backend APIs (OpenAPI) and SQL databases (PostgreSQL, MariaDB, ClickHouse) into hosted MCP servers for AI clients, with built-in dashboards, forms, and credential encryption.

Provides deterministic tools for understanding, transforming, and verifying structured data via MCP, enabling rule inference from examples and verification of transformed records.

A unified backend MCP server that aggregates and filters data from external services like Zaim, GitHub, Google, and Notion, providing cross-cutting views via MCP and REST API without heavy AI inference.

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.

Exposes an agentic AutoML pipeline as a callable tool so any MCP client can upload a CSV and natural language goal and receive a full ML analysis report with SHAP visualizations. Enables autonomous problem understanding, data cleaning, feature engineering, model training, and explainability through a single remote tool call.