"Wikimedia Foundation" matching MCP servers:

Creates narrated 9:16 documentary videos from user-described topics using Wikimedia images and AI voiceover, rendered entirely on your machine.

Provides 11 tools for stock research, including search, market history, financial statements, announcements, and data quality checks, with a local-first architecture using DuckDB and Parquet.

MCP server for a self-hostable typed knowledge graph, giving AI agents durable structured memory with typed nodes, links, and flexible payloads. Enables agents to bootstrap, manage, and evolve their ontology via MCP tools.

Enables on-device generation of Conventional Commits, pull request summaries, and diff explanations through Apple Foundation Models.

MCP server for a personal long-term memory base, providing 140 tools to search and write via PostgreSQL and Qdrant, with integrations for Obsidian, session histories, meetings, tasks, and smart home.

Enables an AI agent to turn a messy CRM account list into a ranked list of accounts to work, with every step authorized, scoped, rate limited, and replayable from an audit log.

Enables users to perform deterministic time series forecasting through Claude by running reproducible Python statistical and foundation models, providing tools for loading data, cross-validation, forecasting, anomaly detection, and exporting re-runnable manifests without any LLM involvement.

Provides a non-custodial USDC wallet on Base for AI agents, with human-in-the-loop approval for every payment.

A starter template for building single-client MCP servers that expose existing REST APIs, deployable via stdio in customer-controlled environments. It provides a structured foundation with validated config, error handling, write authorization, and a mock upstream for local development.

A local, multi-tenant MCP server framework for macOS that consolidates multiple third-party services behind a single MCP endpoint, with a browser-based connection manager. It provides a secure, extensible foundation for adding custom connectors without shipping any built-in integrations.

A production-oriented MCP server foundation for connecting Claude Desktop to social-media and marketing platforms, featuring a reusable connector framework and an optional Meta connector using official APIs.

Connects Claude to on-premise TFS (Team Foundation Server) via REST API, enabling read/write of work items, backlogs, sprints, and pull requests without manual copy-paste.

MCP server for the FLOX trading framework. About 30 tools to run backtests, scaffold strategies, validate for lookahead bias, compute indicators, place orders, and query PnL from Claude/Cursor.

A Model Context Protocol server that integrates Claude with Blender, enabling users to analyze and interact with IFC (Industry Foundation Classes) building models through natural language commands.

MCP server that inspects older repos, identifies stale guidance, and installs a managed foundation for better Copilot work including instructions, objectives, specialist agents, and insights.

A basic TypeScript implementation of the Model Context Protocol (MCP) server designed as a starting point for MCP development. Provides a minimal foundation for building custom MCP servers with stdio configuration for local integration with VS Code and GitHub Copilot.

A safety-first foundation for connecting an MCP client to AutoCAD, currently providing read-only drawing status and health/version/capabilities tools via a Python MCP server and localhost C# bridge.

An MCP server that enables automated dataset creation and custom object detection model training through natural language interactions. It integrates foundation models like GroundedSAM for auto-labeling and supports training specialized YOLOv8 models using local or Unsplash images.

Provides offline search and retrieval of Wikipedia articles using Kiwix .zim files, enabling LLMs to access full Wikipedia content without internet.

Exposes geospatial foundation model inference as MCP tools for LLM agents, enabling building extraction, water segmentation, object detection, change detection, zero-shot scene classification, and embeddings on CPU.