"Wikimedia Foundation" matching MCP servers:

This MCP server provides Wikipedia page view trend data, enabling AI to detect spikes, view historical traffic, and compare cross-platform trends to gauge public curiosity.

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.

An MCP server that gives LLM agents full API access to a VMware Cloud Foundation 9.1 estate, exposing 8,931 operations through eight tools with automatic authentication, async task tracking, and write safety.

MCP server for answering questions about Apache Software Foundation policies, offering tools to list, retrieve, and search policy documents on releases, licensing, branding, security, infrastructure, and incubator topics.

Enables efficient Wikipedia access through a progressive retrieval strategy that minimizes token usage by searching, summarizing, and fetching only relevant sections.

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.

Enables LLMs to search for keywords and fetch full page content from Wikipedia across various languages. It provides direct access to Wikipedia information through search and fetch tools.

MCP server that exposes MAYO Apollo HRM platform's Foundation, Attendance, and Payroll APIs as AI-callable tools, enabling natural language queries for employee profiles, attendance summaries, and payroll reports.

An MCP server that gives LLMs access to Wikipedia, supporting search, article retrieval, summaries, sections, and related topics across multiple languages and language variants.

A Model Context Protocol server that retrieves information from Wikipedia to provide context to LLMs, allowing users to search articles, get summaries, full content, sections, and links from Wikipedia.

A starter template for building MCP servers with SQLite database integration. Provides a foundation with SQL query tools, database seeding capabilities, and a development client for testing.

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.

An MCP server for accessing and analyzing Apache Software Foundation Incubator podling data from podlings.xml. It provides tools to query podling metadata, generate statistics, and analyze incubation trends over time.

A basic Model Context Protocol (MCP) server implementation that provides a foundation for MCP server development. The README doesn't specify particular functionality, suggesting it may be a template or starting point for building custom MCP servers.

An MCP server that enables AI assistants to generate, score, and analyze DNA sequences using the evo2 genomic foundation model. It supports multiple execution modes including local GPU, SLURM clusters, and the Nvidia NIM cloud API for tasks like variant effect prediction and sequence embedding.

Provides tools to interact with the Lineage blockchain API, including wallet seed phrase and keypair generation, balance queries, and latest block retrieval.

Provides Wikipedia page view trend data including spike detection, historical traffic, and cross-platform comparison, enabling AI to access a leading indicator of public curiosity.

MCP server for predictalot, providing streamable HTTP tools for zero-shot time-series forecasting with multiple foundation models and per-type ensembles. Tabular ML endpoints are currently HTTP-only.

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.