"Bower" matching MCP servers:

Enables searching and reading AWS knowledge from sources the official AWS Knowledge MCP Server does not index, including re:Post community Q&A, kiro.dev documentation, and AWS Builder Center posts.

Enables natural-language interaction with Power BI REST API through MCP tools, allowing GenAI applications to access approved business intelligence capabilities and analytics.

Enables semantic search across multiple knowledge datasets using FAISS vector embeddings, allowing natural language queries to find relevant documents with fast retrieval.

Provides 23 production-tested tools for mobile app intelligence, including app metadata, downloads, revenue, rankings, and search, with multi-token failover for uninterrupted API access.

Enables AI agents to answer Power Platform governance questions — data policies, connectors, environments, Managed Environments, security roles, the CoE Starter Kit and ALM — using hybrid keyword-plus-semantic search over Microsoft's documentation, with a deep link to the exact section behind every answer. It runs locally without API keys, offers read-only search, section-reading and document-listing tools, and connects to Claude, VS Code and Copilot Studio.

MCP server for Korean government power/energy statutory plans. Enables AI agents to search and retrieve public power plan documents.

Provides AI models with real-time data about New England's power grid through a Model Context Protocol interface, enabling questions about current power generation, fuel mix, and marginal fuels.

Enables AI assistants to interact with Power BI datasets through natural language, allowing users to query data, generate DAX, and get insights without leaving their AI assistant.

Provides four MCP tools for neural retrieval: personalized user recommendations, item-to-item similarity, cold-start text search, and explainable recommendation scores, enabling LLM agents to query a trained two-tower recommender over Amazon Reviews data.

Enables an AI application to connect to Power BI Desktop semantic models, discover their tables, columns, measures, and metadata, and validate and execute DAX queries so users can ask analytical questions in natural language and receive explained results.