"Learning about PHP programming language" matching MCP servers:

A comprehensive educational server demonstrating Model Context Protocol capabilities for tools, resources, and prompts, allowing AI assistants to connect to external data and functionality.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

MCP server for Durable energy trading analytics that connects Claude Desktop to Snowflake data, enabling natural-language queries about spreads, constraints, generators, and prices.

Bridges Claude (or any MCP client) to manufacturing data via the i3X standard, enabling natural language queries about equipment status, hierarchy, and historical trends.

AI-powered MCP server for connecting and managing Jupyter Notebooks. Enables interactive code execution, multi-notebook management, and multimodal output for data analysis, visualization, and machine learning.

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 natural language queries about grades, GPA, attendance, and schedules by connecting Claude to StudentVue school dashboards. Includes analytical tools for grade simulation, what-if scenarios, and academic performance tracking based on live school data.

Enables you to interact with your Fivetran environment through natural language, supporting read-only queries about connections and syncs, and write operations like updating sync frequency when enabled.

A Model Context Protocol server that allows interaction with the RubyGems.org API to fetch metadata about Ruby packages, search gems, and explore dependencies and ownership information.

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

Enables AI to analyze and transform data using fusion algorithms with statistical, machine learning, or hybrid methods. Provides seamless data format conversion and enhanced analytical capabilities through the Model Context Protocol.

About MCP server exposing 10 APITier utility APIs as AI agent tools — email/phone/VAT validation, UK postcodes, India pincodes, barcode/QR generation, data conversion

Enables AI agents to securely interact with multiple databases (MySQL, PostgreSQL) via natural language queries, with cross-database querying and enterprise-grade security.

Enables AI assistants to perform data analysis on local datasets through natural language, orchestrating Python, R, SQL, Spark, Jupyter, Power BI, and Tableau engines for statistics, machine learning, dashboard generation, and batch processing.

Connects AI agents and MCP clients to Keboola, enabling storage queries, SQL transformations, job triggers, and more through natural language.

Read-only MCP server exposing Android phone timeline data, including events, hourly/daily features, data coverage, and gaps, so Claude can answer questions about observed phone activity.

Provides AI tutoring capabilities for Romanian police academy entrance exam preparation, enabling students to access educational content, track learning progress, and collaborate with peers. Connects to the Academiadepolitie.com platform serving over 50,000 students with comprehensive study materials for law enforcement subjects.

Enables tracing of synthetic events through a toy Redis Streams + Postgres ingestion pipeline, answering questions about which events were dropped, duplicated, delayed, or are in flight, plus overall pipeline health. It also supports first-class fault injection and reconciles observed pipeline state against independently recorded producer ground truth to expose real loss or silent failures.

Enables employees to ask natural-language questions about a client's dbt semantic layer through the chatbot they already use, asking for clarification when needed, refusing when data can't answer, and logging every interaction so the semantic layer improves over time.

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.