"Debugging WebRTC SDK Implementation Issues in Visual Studio 2019" matching MCP servers:

A two-sided marketplace that enables AI agents to buy and sell datasets, prompt packs, and MCP tools using Bitcoin Lightning for instant settlement. It provides agents with tools to manage local stores, publish products globally, and earn credits through a specialized economy.

An MCP server that provides expert knowledge on the OpenAI API, powered by RAG. It enables users to ask technical questions and get accurate answers, with support for search and fetch tools.

Integrates with the Datalog Studio REST API to explore projects, tables, and assets within a workspace. It enables users to understand data schemas and upload plain text content directly for AI processing.

An MCP server designed to assist with generating, converting, and translating Milvus SDK code by retrieving relevant documentation and snippets. It supports PyMilvus code generation, ORM-to-client conversion, and cross-language translation between Python, Java, Go, and other supported languages.

An MCP server that provides deep knowledge about OpenAI APIs and SDKs, enabling users to query technical information through various MCP clients including ChatGPT Deep Research, Cursor, and OpenAI Responses API.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

A full-feature MCP server that serves as a product-development copilot, providing RAG search, SQL queries, and inline chart visualizations from seeded product data, with per-user identity and its own OAuth login.

A toolkit for building Model Context Protocol servers and clients that provide standardized context for LLMs, allowing applications to expose resources, tools, and prompts through stdio or Streamable HTTP transports.

A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.

MCP-driven AI skill capsule search and evolution platform with 900+ pre-built capsules across 31 domains, enabling search, retrieval, contribution, and evolution of skill capsules.

An MCP server that enables AI assistants to access and search Cherry Studio knowledge bases, supporting operations like listing, searching, and retrieving details.

Turn any data source into an MCP server in 5 minutes. Build knowledge bases that AI assistants like Claude and Cursor can query directly.

Enables MCP clients to interact with local LLMs via LM Studio, supporting dynamic chat, vision, RAG, file interaction, and model orchestration.

A lab demonstrating how to deploy a Model Context Protocol (MCP) server and integrate it with Microsoft Copilot Studio, allowing users to connect AI models to different data sources and tools through a standardized protocol.

A Model Context Protocol (MCP) server that enables integration of joke delivery capabilities into Microsoft Copilot Studio and GitHub Copilot, allowing users to request and receive various types of jokes through natural language.

Verifiable document intelligence for AI agents. Extract text, tables, and structured data from PDFs and URLs. Summarize, answer questions, check claims, and translate — all with cited evidence. Store tamper-evident evidence bundles with cryptographic signatures and on-chain attestation via Base L2. Cross-document semantic search and Q&A across named collections. Pay per call with USDC

Provides persistent memory for OpenCode coding agents, allowing them to store and recall architecture decisions, conventions, and gotchas across sessions via semantic search.

Provides persistent memory management for AI agents with 14 tools, including semantic search, context synthesis, auto-context middleware, and write-back actions to external services like Slack.

Provides AI assistants with vector search and persistent memory capabilities using ZeroDB. Enables storing and retrieving context across sessions, semantic document search, and analytics through natural language interactions.