Packages

Memora is a lightweight MCP-compatible memory server that provides persistent semantic memory storage (SQLite with optional cloud sync), knowledge-graph linking, and a graph UI. It supports embeddings-based semantic search, RAG chat with tool calling, and LLM-assisted deduplication/merge workflows.

Personal finance MCP server by [Parsons.AI](https://parsons.ai) — connect AI assistants to your financial data.

Professional MCP server for intelligent subscription management with Gmail + MySQL integration.

The best web search for your AI Agent

Instant markdown sharing. Create, manage, and share markdown documents directly from Claude.

Lean 4 MCP server: compile and prove theorems with Mathlib.

[](https://github.com/mcp-auth/python/actions?query=branch%3Amaster)

> **This local MCP server (`@sanity/mcp-server`) has been deprecated** in favor of the new, improved remote MCP server at [mcp.sanity.io](https://mcp.sanity.io). This repository is archived and no longer maintained.

Provides an MCP server that lets AI agents interact with an Apache Superset instance (manage dashboards/charts/datasets/tags and run/inspect SQL Lab queries) via a set of MCP tools.

A production-oriented Python template for building Model Context Protocol (MCP) servers using FastMCP and FastAPI, including multiple transport protocols (HTTP, SSE, streamable-HTTP), structured logging, environment-based configuration, optional SSL/TLS, OAuth authentication with PostgreSQL token storage (when enabled), and container/OpenShift deployment scaffolding. It also ships with example MCP tools.

Provides a Model Context Protocol (MCP) server that lets an MCP host query YouTube for video search, video details, thumbnails, transcripts, and various channel/playlist metadata (including comments/activities limited to public data).

Aleph is a Python MCP server and CLI/skill that implements a Recursive Language Model (RLM) workflow: it persists working state (loaded contexts, search indexes, evidence, sessions) outside the LLM prompt window, supports iterative search/navigation, server-side code execution over loaded context (exec_python), and recursive sub-query orchestration to converge on answers.

Docmole is an MCP server that helps AI assistants query documentation sites. It supports a local RAG mode (crawl docs, generate embeddings with OpenAI, store/search in LanceDB) and a Mintlify mode (proxy to Mintlify’s assistant API for certain Mintlify-powered documentation sites). It also provides a CLI to set up projects, serve/stop them, and list available projects.

Provides an MCP (Model Context Protocol) server that wraps the dufs API to perform file operations (upload, download, list/search, delete, create directory, move/rename, compute SHA256, and download a folder as a ZIP) using either stdio (MCP-native) or HTTP/SSE endpoints. Supports Basic Auth and configuration via environment variables.

Provides an MCP server with tools to estimate Google Cloud (GCP) running costs by querying Google Cloud Billing Catalog API for SKUs/pricing and (separately) fetching free-tier information from GCP documentation, then calculating estimated monthly costs with automatic free-tier deduction.

A Go SDK to build MCP (Model Context Protocol) servers, letting developers expose MCP tools, resources, and prompts over multiple transports (stdio and HTTP with SSE), while handling MCP protocol message routing and lifecycle in accordance with the MCP specification.

Provides a Go-based MCP (Model Context Protocol) server that exposes markdown files from a local filesystem (via fs.FS) as MCP resources (file:// URIs) and tools to list and read markdown files. Supports YAML (---) and TOML (+++) frontmatter parsing and returns frontmatter metadata in tool outputs/resource descriptions.

mcp-batchit is an MCP “aggregator” server that exposes a single tool, `batch_execute`, to batch multiple downstream MCP tool calls into one JSON-RPC request. It spawns/connects to a target MCP server (e.g., filesystem) and runs sub-operations in parallel up to `maxConcurrent`, with per-op `timeoutMs` and optional `stopOnError` behavior, returning a consolidated result array.

An MCP server that gives AI agents full GUI control over remote macOS machines via Apple Screen Sharing, enabling screenshot capture, keyboard/mouse input, application launching, and drag-and-drop — all without installing software on the target Mac.

Provides an MCP server that helps orchestrate sequential problem-solving by analyzing available MCP tools and returning confidence-scored recommendations (with rationale, priority, and suggested inputs) for each thought/step, including step tracking, branching/revision support, and built-in memory/history limits.