"AT&T" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Engram is a persistent, long-term memory layer for AI agents and assistants. Claude, ChatGPT, Grok, Cursor and any MCP client share one memory, stored as plain markdown notes: your knowledge base, second brain and AI context in one place. No extraction step: the memory is the note itself, so you can read exactly what your AI remembers and fix it. Edit your memory in Obsidian (real-time sync), the web app, or on your phone. Hybrid keyword + semantic search (RAG over your notes) finds exact strings like error messages, config keys and IDs. Remote MCP server over Streamable HTTP with OAuth 2.1; notes encrypted at rest. Https://engram.page https://youtu.be/rwnPeZ-8Lqo?is=NI-N7BduydGAiZlF https://github.com/engram-app/Engram
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.
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.
Project memory, tasks and Telegram notifications for your coding agent. Chip account required.
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.
**ColdState Knowledge Search MCP Server** https://github.com/daniel-coldstate/coldstate-mcp Semantic search over 64.6M knowledge entries — the structured alternative to web search APIs and web scraping for LLM agents. No crawling, no rate limits, sub-3s responses. Cloud-hosted at services.coldstate.ai
**Swiss B2B sales and everyday-AI know-how — inside your AI assistant.** 113 free 5-minute learning modules from [latzerus.ch](https://www.latzerus.ch). No account, no API key, no cookies. `https://mcp.latzerus.ch/mcp` --- **`lernmodule_suchen`** — search all modules. Understands paraphrases, synonyms, plural forms and typos. **`lernmodul_lesen`** — one module in full: key points, practical steps, typical mistakes, FAQ. **`lernmodule_uebersicht`** — everything grouped by theme, or just one theme. **`ueber_latzerus`** — what the project is and who is behind it. --- **Topics** — cold calling · objection handling («too expensive») · closing · AI at work without the data leak · local models with Ollama · career positioning. **The modules are written in German.** So are the tool names — your assistant handles that. --- **Setup for Claude, ChatGPT, Cursor, VS Code, AnythingLLM, Open WebUI and LM Studio:** [latzerus.ch/mcp](https://www.latzerus.ch/mcp/) **Source, MIT:** [github.com/kriswindu/latzerus-mcp](https://github.com/kriswindu/latzerus-mcp) Knowledge project of Christoph Latzer, St. Gallen / Zurich. Quoting welcome — please name the source.
Give your AI agent persistent, governed memory for every project. At task start it recalls the approved decisions, conventions, risks and architecture (semantic search, ranked by importance); at close it proposes what was learned as typed memories that you review and approve — governance, not a notes dump. Agents propose, humans govern: edits go back to pending and deletion is human-only by design. Connect Claude Code, Cursor, Claude Desktop or any MCP client in two minutes with just your API key — hosted (nothing to install) or locally via `uvx solucortex-mcp`. Built by SoluAI and dogfooded daily: SoluCortex is developed using its own living memory.
Query core AI knowledge, essays, executable Labs, Homeric data and epistemic claims.
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.
Persistent memory for AI agents to retain, retrieve, and recall conversation context through MCP.
Public, read-only MCP over the complete 64-hexagram I Ching corpus. Deterministic lookup only.
Constitutional AI kernel with 13 MCP tools, 888_JUDGE verdict pipeline, and VAULT999 ledger.
Open scientific and engineering knowledge for AI agents: search, evidence, document publishing.
Shared memory for AI agents. One address per fact, one signature you check. No key to read.
Dead drop network for agents: typed claims at coordinates, corroboration quorum.
Search and cite the full Pāli Canon (Tipiṭaka, ~444K segments) — Sutta, Vinaya, Abhidhamma at parity with SuttaCentral. Hybrid search, full-sutta fetch, translation comparison, Pāli word lookup. Free, non-commercial, offered as Dhamma Dāna.
