"A tool for storing and accessing shared memories across multiple Claude.ai users" matching MCP servers:

Enables AI coding agents to collaborate on the same project by sharing session briefs and reading each other's native transcripts, memories, and instructions in place, with zero-copy, across different agent tools.

Local MCP server enabling Codex and ChatGPT to read/write files, execute commands, manage processes, use Git, and inspect images on the user's machine with full privileges.

Captures code build sessions and publishes them to a shared team knowledge base, making past work discoverable via semantic search.

Enables AI assistants to manage multiple Outlook/Microsoft 365 mailboxes simultaneously through MCP tools, with local OAuth token storage.

Connects several Gmail and Google Workspace accounts to MCP-capable AI apps so they can search, read, draft, send, reply, and label mail across one account or all of them at once. Runs locally on Windows with encrypted on-disk credentials, no delete tools, and approval-gated sending.

A portable, self-hosted semantic memory layer for LLM agents, enabling them to search and add memories by meaning over the Model Context Protocol.

Shared Margin is a Codex-native room for reading books and watching films together one moment at a time. It enables human and GPT-5.6 to linearly experience media, capture anchored observations, and maintain a durable trail.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

Creates a summary of photos from Apple Photos, generating a personalized year-in-review wrapped experience based on your photo library.

Enables several Claude windows or accounts to save and search a common set of notes, preferences, and decisions, and to leave topic-filed messages for each other that the recipient reads on demand. All data stays in two local JSONL files with no network access, so multiple instances can share context and hand off work without repeating background.

A Firecrawl MCP server with built-in support for multiple API key rotation, enabling web search, scraping, crawling, extraction, and more via MCP clients.

MCP server that serves tools for interacting with a users API over stdio, supporting operations like finding users. It provides a validated configuration and structured logging for building MCP-based user management workflows.

Enables stdio-capable MCP hosts to store and retrieve memories in a caller-specified private directory, keeping program repositories separate from memory data.

Enables people and AI assistants to collaborate in a shared page where agents inspect room state and prepare structured drafts, while humans keep final authority over commitments and confirmations.

Enables multiple coding-agent sessions (Claude Code, Codex, and other MCP clients) in the same repository to share a single Serena server and its language servers, instead of each session starting its own. This reduces memory and CPU use, speeds up session startup, and prevents separate instances from overwriting each other's cache.

Enables a human and an AI to collaborate on shared sketches and perler-bead projects, with tools for drawing, bead placement, finishing, exporting, and gallery display.

Enables AI agents to call tools through a security gateway that intercepts requests, enforces deterministic policy and risk checks, blocks or escalates high-risk actions for human approval, and keeps tool credentials and audit logs protected.

Lets multiple AI agents share a single always-on, logged-in Chrome instance over MCP, giving each session its own window, waiting until pages are fully ready, preserving logins across agents, and handing a window to a human for CAPTCHAs or 2FA.

Enables AI agents to create password-protected, link-shareable lists and then read, edit, and restyle them — managing typed columns, rows, and per-list CSS, and looking up product pages to fill in prices, photos, and names. It exposes eighteen tools over Streamable HTTP with per-agent bearer tokens, so agents can build and maintain a shared list end to end.

A zero-dependency MCP server that enables multiple AI agents to share key-value data, append-only timeline notes, and semantic vector memory across sessions and processes.

Enables multiple AI agent sessions and sub-agents to share single running instances of configured tool backends, reducing process and memory overhead, stopping idle backends automatically, and exposing a compact set of meta-tools for discovery and invocation.

Enables Claude to create, open, and edit persistent tldraw whiteboards in real-time, with live collaboration between human and AI.

Enables LLMs to perform addition calculations through a stdio-based MCP server with an add_numbers tool and type-safe validation.

MCP server for a shared Postgres-backed knowledge base with hybrid retrieval and agentic RAG, enabling coding agents to upload, search, and ask questions over documents with cited answers.

Aggregates multiple shared MCP servers into a single HTTP gateway, providing a unified, observable, and reusable MCP access layer for multiple AI clients like Codex, OpenCode, and OpenClaw. It centralizes configuration management, logging, health checks, and circuit breaking to simplify multi-client MCP deployments.

Enables claude.ai custom connectors to run SQL-like gviz queries against Google Sheets through a shared AWS-hosted MCP server protected by Cognito Google login, with room to add more tools over time.

A Model Context Protocol server that provides a scratchpad tool for LLMs to organize thoughts, take notes, and plan approaches to complex problems.

A public, source-linked notebook for AI agents and people. Search and read without a key; publish findings, questions, and corrections with permanent IDs and linked replies using an operator-controlled posting key.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Enables AI agents to search and read linked Markdown notes, queue review questions, capture feedback, and propose edits with stale-edit protection.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.

Provides a shared context layer for AI agent teams to improve token efficiency through context deduplication and incremental state sharing. It enables multiple agents to coordinate tasks, share real-time discoveries, and manage dependencies while significantly reducing redundant data transmission.

Enables Codex, Claude Code, and other MCP-capable clients on the same computer to share local SQLite long-term memory with Chinese keyword search, project scoping, revision history, and Markdown export. It does not automatically capture conversations or synchronize across devices.

A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.

提供A股实时行情、历史K线、财务报表、技术指标等全方位数据查询的MCP服务器，支持盘中即时更新及批量查询。

Enables cross-session memory, weighting, and factory/PR/artifact linking for CIC agents, aggregating multiple sessions to identify promotion candidates.