"Help with Low-Level Programming Languages, C#, and .NET" matching MCP servers:

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.

A multi-protocol API server that exposes a single ORM schema over 11 transports including MCP, enabling AI agents to interact with data from 13 different databases via a unified interface.

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 assistants to validate email addresses, track validation tasks, view per-address results and account analytics, and export cleaned email lists through the UChecker API.

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.

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

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.

Provides free read-only business checks for IBANs, EU VAT number format, Peppol e-invoice rules, supplier bank-detail changes, and CSV/XLSX data cleaning.

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.

Bridges Microsoft Clarity's analytics API with Claude, enabling custom date ranges and page-level filtering on top of Clarity's native limitations.

Enables environmental scientists to tidy PFAS lab files, screen results against two drinking-water guideline sets side by side, search public guidance with page-level citations, and check report text so every number traces back to its lab row or guideline page.

Enables AI tools to normalize messy payroll spreadsheets (xlsx/xls/csv) into a standardized 10-column template for social insurance calculation, with automatic column detection, net-to-gross conversion, and cross-entity/month merging.

Enables SQL lineage analysis, including table-level and column-level lineage, field-level tracing, and support for multiple SQL dialects via sqlglot.

Enables LLM agents to interact with a SQLMesh project, providing model metadata, column-level lineage, plan previews, audits/tests, and environment diffs, with optional apply/run actions.

Vizro is a low-code toolkit for building high-quality data visualization apps.

An MCP server that enables LLMs to detect and anonymize over 25 types of Personally Identifiable Information (PII) using Microsoft Presidio. It supports various redaction strategies and can process both plain text and structured data to help ensure data privacy.

Enables converting data structure definitions between numerous schema formats and generating code for multiple programming languages, using Apache Avro Schema as the integration model.

Enables AI agents to propose and validate data-cleaning recipes, then receive four-way DuckDB comparisons that separate data, recipe, and interaction effects with exact decimal attribution and row-level provenance.