Files and storage
Box
By Box
Box's official MCP server lets AI search and read files, ask questions across documents with Box AI, extract data and upload files.
- Official
MCP server category
Knowledge and memory MCP servers
Give assistants a memory and access to the knowledge your team has written down.
- 14 MCP servers
Knowledge and memory servers do two related jobs. Some give an assistant a place to store and recall facts between conversations. Others connect it to wikis and note systems, so it can find what the team has already written.
Both make assistants more useful over time, and both need clear rules about what may be stored and who can see it.
Knowledge and memory
All knowledge and memory mcp servers
Databases
Chroma
By Chroma
Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
- Official
Knowledge and memory
Confluence
By Atlassian
Atlassian's official Rovo MCP server lets AI search and read Confluence spaces and pages, create and edit content and add comments and labels.
- Official
Developer tools
Context7
By Upstash
Upstash's Context7 MCP server gives coding assistants current, version-specific documentation for thousands of libraries.
- Official
Productivity
Jira
By Atlassian
Atlassian's official Rovo MCP server lets AI search, create and update Jira issues and search, read and create Confluence content.
- Official
Knowledge and memory
Memory
By Model Context Protocol project (reference server)
The MCP project's Memory reference server gives AI a local knowledge graph to store people, things and facts and recall them later.
- Official
Productivity
Notion
By Notion
Notion's official hosted MCP server lets AI search and read pages, create and update pages and databases, query data and add comments.
- Official
Knowledge and memory
Obsidian
By Markus Pfundstein (community)
A community MCP server that lets AI list, read, search, edit, append to and delete notes in an Obsidian vault through the Local REST API plugin.
- Community
Databases
Pinecone
By Pinecone
Pinecone's official developer MCP server lets AI search Pinecone docs, manage indexes, upsert records, search and rerank.
- Official
Databases
Qdrant
By Qdrant
Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
- Official
Knowledge and memory
Readwise
By Readwise
Readwise's official hosted MCP server lets AI search and manage your reading highlights and Reader documents.
- Official
Databases
Redis
By Redis
Redis's official MCP server lets AI read and write Redis data structures, search vector indexes and inspect server information.
- Official
Developer tools
Sequential Thinking
By Model Context Protocol project (Anthropic reference servers)
The MCP project's Sequential Thinking reference server gives AI a structured way to break a problem into steps and revise them.
- Official
Databases
Weaviate
By Weaviate
An MCP server built into Weaviate that lets AI read collection settings, list tenants, run hybrid search and, when allowed, upsert objects.
- Official
How to choose
How to choose in this category
For company knowledge, connect the wiki you already use and respect its permissions. For memory, decide what the assistant may remember, how long and how a person can review or delete it.
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Related on ExecMedia
Related services
View all related services- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
- RAG knowledge assistants Assistants that answer questions from your own documents and show where each answer came from.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- Headless CMS Content managed in one place and published to your website, app and other channels through an API.
Solutions
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View all industriesGuides and articles
View all guides and articlesAI models
View all ai models- Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
- Gemini Embedding 2 Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
- text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
- Cohere Embed v4 Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
- Cohere Rerank 4 Cohere's multilingual rerank models, which sort search results by relevance, in a best-quality Pro version and a low-latency Fast version.
Glossary terms
View all glossary terms- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- Chatbot A chatbot is software that holds a conversation with people through text or voice, answering questions or completing simple tasks.
- Hallucination A hallucination is when an AI model states something false or invented as if it were true, such as a made-up fact, figure or source.