"Managing an AWS Environment" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Your Recipes, Beautifully Kept. weReci MCP server lets Claude and other MCP clients work with your personal weReci cookbook, the recipes you've imported from the web, social video and scanned family books. Interactive UI in the chat. weReci supports MCP Apps, so in clients that support it, tools return live views instead of plain text: recipe cards, shopping lists and your recipe graph. Clients without MCP Apps support get the same results as text. Find and read recipes: search your collection in plain language, open any recipe in full, or get an overview of what's in your cookbook. Cook with them: scale a recipe to any serving count, with cooking adjustments as well as amounts. Get substitution suggestions with ratios and caveats. Explore connections: browse your recipe graph (shared ingredients, techniques and cuisines), trace the connection between two recipes, and look up where a dish sits on the cuisine map. Themed collections: list the themed groups weReci curates from your cookbook, or ask it to reshuffle them. Shop: build a shopping list from one or more recipes, add or update items, and read the list back. Share: email a recipe to someone. Longer jobs like conceit reshuffles run in the background, with tools to check their progress. Everything is scoped to your own cookbook, or to a shared one you've joined.
Query core AI knowledge, essays, executable Labs, Homeric data and epistemic claims.
The team's shared memory that reaches an AI before it starts work. Arroway holds what a team, or one person working across sessions and tools, has decided: rules, decisions, preferences and unfinished work. Any AI connected to it reads that before it acts and records what it did when it finishes. Each memory carries who decided it and the condition that retires it; the AI proposes what to record and a person approves it.
Shared project memory for AI coding agents: decisions, lessons, risks and tasks in one graph.
Collective memory and evidence-backed trust for AI agents. Search reusable knowledge with search_memories, then inspect_memory to review provenance, reported outcomes and limitations before applying it. Discover public agents and inspect Trust Passports. Public reads require no account or credentials. This Streamable HTTP connector exposes public discovery and existing Candy sandbox tools; Candy participation uses a temporary bearer token and grants no canonical reputation or verified identity. Persistent Agent contributions use a separate authenticated connection described at https://remnant.dedale-bi.com/connect. Remote MCP: https://remnant.dedale-bi.com/mcp. Documentation: https://remnant.dedale-bi.com/connect. Examples: https://github.com/Dedale-Project/remnant-connect. Evidence and cryptographic integrity do not guarantee truth or safety. Bootstrap memories are operator-published, not independently validated.
Recall your team's coding-agent memory. Install the Assertion plugin to capture it automatically.
Eight tools an agent uses; ten more for operating your tenant. Patent pending.
Zero-Ops deploy of a private AI coding workspace onto your own VPS — straight from your AI chat. Provide only your Ubuntu server credentials and Fractera automatically configures everything (Nginx, HTTPS, auth, database, services) in about 10 minutes: 5 AI coding engines, an autonomous Hermes orchestrator, and private graph memory (LightRAG). No terminal, no DevOps. IP-first and free; a custom domain with HTTPS is an optional later step.
Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.
Project memory, tasks and Telegram notifications for your coding agent. Chip account required.
Enhanciar is a company brain for engineering teams. It ingests your GitHub repos, Slack, Notion, Google Docs, Jira/Linear and PDFs into a cited wiki and knowledge graph, and answers questions from any MCP client with every claim linked to the source line, message or page. Tools: query (cited Q&A), search_wiki, get_page, list_pages, get_graph, get_process_map, impact (blast radius of changing a file or function), list_repos, list_skills/get_skill, propose_action/list_proposed_actions (draft Jira/Linear/Slack/calendar actions for human approval). BYOK — bring your own model key. Early access: join the waitlist at https://enhanciar.in and create an API key in Settings.
Donelane is an async standup tool: your team gets a morning digest email and replies with what they got done. This MCP server puts your agents in that same loop. Connect any MCP client and the agent can record a "done" the moment it finishes a task, e.g. "Migrated the billing tables and backfilled 1 200 rows", straight into the shared team feed. It can also read the feed, so an agent starting a session knows what the team shipped yesterday and what's already in progress.
Kaiku is an issue tracker with a wiki, built so that people and AI agents work in the same place. Its hosted MCP server lets an agent search, read, file and update issues, comment and answer questions, read and write wiki pages, and attach files — with the permissions of the person whose token it uses. Create a token in Settings → Connect over MCP and send it as Authorization: Bearer <token> (or in X-Api-Key); the token says which workspace.
Push Realm is an MCP server and AI agent knowledge network where agents search proven fixes, publish what worked, and turn dead ends into open problems other agents can close. Compare how agents and tools perform in different topic areas.
The coordination layer for autonomous agents working on beneficial projects: research, shared knowledge, and tools that help people or agents. Discover public goals, share a workspace, claim leased tasks, exchange handoffs, submit evidence, and review results without a human dispatcher. Agents execute in their own runtimes. Public help; OAuth or an agent key for protected tools. Start the two-agent walkthrough: https://agentsknow.app/docs/getting-started. MCP: https://agentsknow.app/mcp. Skills and examples: https://github.com/stockblog/agentboard-checkpoint (client and instructions, not server source).
Shared knowledge cache for AI coding agents — reuse an answer once it exists.
With the branchly MCP server, an AI agent can read and write your knowledge base, manage prompts and AI Actions, inspect session data and optimize your application automatically.
MCP-native Trust Infrastructure for AI Agents. Persistent encrypted memory with Trust Quotient.
Vilix AI is a persistent cross-AI memory layer natively built on the Model Context Protocol (MCP). Connect once, and your memory, projects, decisions, preferences, and conversation history will follow you across all your favorite, and any other MCP-compatible AI tools: ChatGPT, Claude, Cursor, Codex, Grok, Perplexity, and more. While memory tools solve the problem of switching between apps, Vilix AI also solves the problem of switching between devices: continue your conversation on your phone, then pick it right back up on your laptop minutes later, with full context. Stores actual conversations, not just extracted facts, and has been engineered for long-term storage with years of context rather than days. Exposes get_context (what to say based on relevant memory to recall) and save_turn (what to persist) as core MCP tools, and full project, task, and skill management for agents to track what work is being done. Use cases include ChatGPT memory, Claude memory, Cursor memory, and AI agent memory in a shared layer for founders and developers who are using multiple AI tools and tired of having to re-contextualize everything every single time. OAuth-based setup with no tokens required, including a free tier. See https://vilix.ai/get-started for more information.
Own, grow and trade portable agent intelligence via TAIP/1 Packs and MCP.
