"Information about Unreal Engine 5 (UE5)" matching MCP servers:

Enables AI agents to safely operate on PostgreSQL data through typed MCP tools with per-identity RBAC, guardrails, and immutable audit logging.

Enables AI assistants to query and manage a local Engine DJ library on macOS, including searching tracks by BPM, key, genre, or comments, auditing duplicates and missing files, reading cues and beatgrids, and building playlists or fixing tags when write access is enabled.

A provably read-only SQL layer for AI agents (MCP) and weekly reports — over the database behind an ERP (Logo Tiger, Netsis, Mikro). Statement-checking guard, measured against 28 attacks.

Enables natural-language-driven analytics over CSV/Parquet or built-in data by decomposing questions into analytical tasks, running them via deterministic DuckDB and SciPy tools, and publishing only claims verified against the underlying rows.

Enables an event-driven operations agent to query internal SQL, GitHub, Slack, Jira, documentation, and Kubernetes telemetry through 28 RBAC-gated MCP tools, with shared memory for playbook retrieval and incident workflows.

An MCP server that exposes personal financial data — transaction ledger, portfolio holdings, live/historical market prices, and quantitative risk metrics — as standardized tools, resources, and prompts, enabling natural language reasoning over real computed numbers.

Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

Generates cryptographically secure, compliant UUIDs (v4, v5, v7) to prevent AI-hallucinated identifiers that would cause database collisions.

Retrieves comprehensive patient information including personal details, insurance plans, and employee data through the get_patient tool. Supports Docker Compose deployment and integration with Watson Orchestrate for healthcare data access.

Enables local AI-powered retail location intelligence by exposing database schema and spatial query tools to LLMs. It supports executing validated SQL, retrieving example spatial analyses, and generating strategic business insights from retail siting data.

Enables AI agents to run deterministic database intelligence tasks including natural-language-to-SQL synthesis, AST SQL auditing and optimization, schema migration diffing, SQL transpilation, index and normalization advice, PII scrubbing, fixture generation, connection-pool sizing, isolation-hazard auditing, and ORM model compilation over Streamable HTTP or stdio.

MCP server that enables AI agents to interact with Spica servers, managing databases, serverless functions, storage, authentication, auditing, debugging, and version control.

An MCP Server implementation that integrates the Balldontlie API, to provide information about players, teams and games for the NBA, NFL and MLB.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

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

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

An MCP server that provides detailed information about your development environment to the Cursor code editor, enabling more context-aware assistance.

A Python-based server that interacts with EPICS process variables, allowing users to retrieve PV values, set PV values, and fetch detailed information about PVs through a standardized interface.

An MCP server that provides standardized access to Pokemon data, allowing users to search, compare, and retrieve detailed information about Pokemon through natural language tools.

Enables AI-powered natural language queries about BIT Information Portal (BIP) data by integrating with BIP APIs through session management. Uses Google Gemini to intelligently route questions to appropriate endpoints and generate contextual answers about students, departments, courses, faculty, and achievements.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

Enhanced knowledge graph memory server for AI assistants that uses Neo4j as the backend storage engine, enabling powerful graph queries and efficient storage of user interaction information with full MCP protocol compatibility.

Enables conversational spatial SQL queries in plain English, turning natural language questions into validated PostGIS operations and rendering results as GeoJSON on an interactive map.

A server that allows AI agents to query and retrieve information about upcoming events at Berghain nightclub through a DynamoDB-backed FastAPI service.

Enables searching and retrieving detailed information about Swedish companies, including financial data and annual reports from Bolagsverket (Swedish Companies Registration Office), with intelligent caching for fast responses.

MCP server that allows AI assistants to query and retrieve information about office supplies inventory from a CSV file.

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

Enables AI agents to answer analytical questions about an online store's SQLite database through specialized read-only tools, without any risk of modifying the underlying data.