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This Anti-Bullshit MCP server, developed by Teglon Labs, provides tools for analyzing claims, validating sources, and detecting manipulation using multiple epistemological frameworks. Built with TypeScript and leveraging the Model Context Protocol SDK, it offers empirical, responsible, harmonic, and pluralistic approaches to evaluate information. The implementation focuses on rigorous fact-checking and critical thinking, making it accessible for AI models to assess the credibility and implications of various claims. It's particularly useful for tasks like academic research validation, media literacy enhancement, or combating misinformation in digital content, enabling AI assistants to apply nuanced epistemological analysis without requiring deep expertise in each framework.

An MCP server template for Cloudflare Workers with zero runtime dependencies, providing a hand-rolled Streamable HTTP transport that lets you easily define and deploy tools with static bearer token auth.

Enables AI assistants to query BuiltWith's technology detection data directly, supporting natural language questions about website technologies.

Query money flows between state, citizens, and enterprises. Simulate policy changes, compare cohorts, and check data confidence directly from MCP-compatible tools.

A local MCP server that enables managing Google Contacts via the People API through tools for listing, searching, getting, creating, updating, deleting, and counting contacts. It requires an externally stored OAuth token and confirms all write operations.

MCP server enabling AI assistants to interact with the Qualys vulnerability management platform. Supports investigation, risk assessment, compliance, remediation, security overview, and report management.

Enables running a remote MCP server on Cloudflare Workers with OAuth login, allowing AI clients like Claude to interact with tools via SSE.

This MCP server provides AI-powered restaurant discovery and booking assistance by integrating Google Maps Places API with intelligent recommendation algorithms. Built with TypeScript using the Model Context Protocol SDK, it offers restaurant search with cuisine filtering and location-based queries, AI-driven recommendations that score restaurants based on mood, event type, and user preferences, detailed restaurant information retrieval including reviews and photos, availability checking, and booking instruction generation. The implementation features a sophisticated scoring system that weighs factors like ratings, cuisine matches, price levels, and event suitability to provide contextually relevant restaurant suggestions, making it valuable for dining planning, date night coordination, business meeting venues, and family gathering locations.

This MCP server implementation, developed by Hal, provides a Python server for WeCom (WeChat Work) bots following the Model Context Protocol. Built with FastAPI and leveraging libraries like fastmcp and httpx, it offers a streamlined interface for integrating WeCom bot functionality into MCP-based applications. The implementation focuses on simplicity and ease of use, making it ideal for developers looking to add enterprise messaging capabilities to their AI workflows. It supports core WeCom bot features and can be easily extended. This server is particularly useful for teams using WeCom who want to automate notifications, create interactive chatbots, or integrate AI-assisted communication tools within their existing enterprise messaging infrastructure.

Enables AI agents to discover WordPress plugin opportunities by querying the WordPress.org directory for abandoned, low-rated, or unsupported plugins that can be replaced or out-built.

MCP server for interacting with FreshRSS via the Google Reader API, enabling subscription management, content reading, and item operations.

Enables natural language querying of Korean public medical data (data.go.kr) through MCP clients like Claude, providing tools for searching hospitals, drugs, non-payment costs, disease statistics, and more.

MCP server giving AI agents controlled residential-proxy web access with optional browser rendering and structured extraction from 60+ sites.

Enables LLMs to manage test cases, cycles, and executions in Jira Data Center via Zephyr Scale.

Control real, physical iPhones with AI: screen reading, human-like touch, no jailbreak.

Provides read-only access to Google Docs and Google Sheets, enabling users to read document content, spreadsheet data, and list/search accessible files through natural language.

This MCP server implementation provides semantic search capabilities over AWS Cloudscape Design System documentation using vector embeddings. Developed by Praveen Chamarthi, it uses the Jina Code Embeddings model and LanceDB for local vector storage to enable efficient documentation retrieval. The server exposes two complementary tools: cloudscape_search_docs for finding relevant documentation files based on natural language queries, and cloudscape_read_doc for reading the full content of specific files. The implementation features hardware-optimized embedding detection with support for Apple Silicon MPS, CUDA acceleration, and CPU fallback, along with lazy loading of resources and deduplication of search results for token efficiency in agentic workflows.

Provides a tool to fetch real-time cryptocurrency prices from the Binance API and logs server activity to file resources. It allows users to query crypto symbols and monitor MCP interactions through natural language interfaces.

MCP server for on-page SEO auditing and JSON-LD schema validation, providing tools for full SEO audit, schema validation, heading hierarchy, and image audit without requiring API keys.

Provides access to Gmail, Google Calendar, Google Docs, and Google Sheets via OAuth 2.0 authentication. Supports granular per-service permission levels (read-only, read+write, full access) for fine-grained access control. Built with TypeScript and MIT licensed.