"Learning NestJS Framework Knowledge" matching MCP servers:

MCP server for querying procurement and inventory documents (invoices, purchase orders, etc.) via structured lookups, precomputed comparisons, and full-text search.

A production-grade MCP server framework providing multiple specialized servers for tabular data, documents, calendars, markdown, prompt engineering, and general utilities.

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

Provides MCP tools for AI assistants to retrieve compact daily futures data packets, submit manual market data, run whitelisted crawlers, search research summaries, and save/read historical reports on demand for caustic soda, PVC, and coking coal futures.

Provides tools for managing qualitative research knowledge graphs, enabling structured representation of research projects, participants, interviews, observations, codes, themes, and findings.

Enables AI assistants to interact with security data pipelines and map data to the Open Cybersecurity Schema Framework (OCSF) through Tenzir. Provides seamless integration for processing and analyzing cybersecurity data using natural language.

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.

MCP server for the BAIC Data Layer. Provides tools to manage sources, knowledge bases, knowledge clusters, run data agents, and create/manage pipelines.

An MCP server that provides data visualization and machine learning tools, featuring automated intent-based pipeline routing for data cleaning and model training. It enables LLMs to process CSV or JSON data to generate visual charts, perform regressions, or execute clustering analysis.

A Model Context Protocol server enabling AI assistants to interact with TriliumNext notes, providing tools for creating, searching, reading, and updating notes in your knowledge base.

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.

Provides tools for named entity extraction, Wikidata disambiguation, sameAs schema linking, and knowledge graph triple construction, enabling structured semantic data processing.

Enables reading, summarizing, searching, validating, and surgically editing Pentaho Kettle .kjb/.ktr files without requiring a PDI installation or executing them, using an embedded knowledge base.

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.

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.

A cryptocurrency price service that provides real-time crypto pricing information through an MCP (Model Context Protocol) framework with CoinMarketCap API integration.

Enables local offline machine learning workflows, including CSV profiling and cleaning, classifier and neural network training, hyperparameter optimization, evaluation metrics, confusion matrix plots, Streamlit dashboards, and HTML reports through MCP tools.

Enables comprehensive SQLite database management through natural language including database creation, table operations, data CRUD operations, backup/restore, and CSV import/export functionality. Built on FastMCP framework with PySqlit library for reliable database interactions.

A conversational copilot that guides users through the Knowledge Discovery in Databases (KDD) process to perform automated data profiling, analysis, and modeling via natural language. It enables the creation of interactive dashboards and manages the end-to-end analytical workflow within a secure sandbox environment.

Enables machine learning data preprocessing pipeline creation and model training, deployment, and prediction for Teradata databases.