HubSpot MCP Server

Public, read-only MCP over the complete 64-hexagram I Ching corpus — hexagrams, trigrams, changing lines, and reading context from an original English translation. Deterministic lookup only; no casting or divination.

Read-only database access with schema inspection

A Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.

Retrieving and analyzing issues from Sentry.io

MCP server for ArcGIS Portal and ArcGIS Online. Lets AI assistants search content, query feature layers, manage features, handle content operations, and administer users and groups. Built on the Model Context Protocol for integration with Claude Desktop, Cursor, VS Code Copilot, and other MCP clients. Disclaimer: This is an independent open-source project. It is not affiliated with, endorsed by, or sponsored by Esri Inc. "ArcGIS" is a registered trademark of Esri.

BlenderMCP connects Blender to Claude AI through the Model Context Protocol (MCP), allowing Claude to directly interact with and control Blender. This integration enables prompt assisted 3D modeling, scene creation, and manipulation.

Location services, directions, and place details

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

ContextBridge (CB) is a local-first retrieval layer that sits between your codebase and your AI coding agent. Instead of pasting raw source files into context or letting the AI guess which files matter, CB indexes your codebase's real structure (via Graphify) and returns a compact, ranked result — owner file, related files, key symbols, and dependency chains — grounded in your actual code. A typical response is ~4–8 KB instead of the 100+ KB of raw source an AI would otherwise need to read to answer the same question — roughly a 96% cut in input tokens sent to your cloud AI, with zero hallucinated file paths or method names.

Zhipu Web Search MCP Server is a search engine specifically designed for large models. It integrates four search engines, allowing users to flexibly compare and switch between them. Building upon the web crawling and ranking capabilities of traditional search engines, it enhances intent recognition capabilities, returning results more suitable for large model processing (such as webpage titles, URLs, summaries, site names, site icons, etc.). This helps AI applications achieve "dynamic knowledge acquisition" and "precise scenario adaptation" capabilities.