"Using Logseq data as context for chat applications" matching MCP servers:

Interacts with LogSeq via its API.

Connects MCP-compatible AI assistants to Logseq graphs, enabling block-first operations, block references, and context graph building.

Enables AI to automatically search, retrieve, and organize your Cursor chat history across sessions. Supports tagging, nicknames, project-scoped search, and full-text search to maintain context between conversations.

A Model Context Protocol server that enables AI agents to interact with a local Logseq instance, allowing operations like creating pages, managing blocks, and searching across a knowledge graph.

A Model Context Protocol server that enables AI agents to interact with local Logseq knowledge graphs, supporting operations like creating/editing pages and blocks, searching content, and managing journal entries.

An MCP server for interacting with Logseq graphs, enabling AI assistants to read, create, and manipulate Logseq content.

A server that enables LLMs to programmatically interact with Logseq knowledge graphs, allowing creation and management of pages and blocks.

Enables AI agents to read Logseq notes, journals, tasks, pages and blocks—including embedded screenshots as viewable images—directly from the on-disk graph. It works read-only with Logseq closed and no network access, snapshotting the database so nothing can be modified.

Python stdio MCP server that interfaces with the Logseq local HTTP API, enabling tools to manage pages, blocks, queries, and graph configurations in Logseq.

Enables external AI assistants to interact with Logseq graphs through 34 specialized tools for managing pages, blocks, journals, and tasks via the local HTTP API.

Enables interaction with your Logseq personal knowledge management system via MCP, allowing retrieval of tagged notes and todo lists through Logseq's HTTP API.

Enables AI assistants like Claude to directly read, write, search, and navigate your local Logseq knowledge graph, including managing journals, pages, backlinks, and page relationships without manual copy-pasting.

Connects AI assistants to Logseq knowledge graphs to read, write, and search pages, blocks, and journals via the Model Context Protocol. It features 17 tools for full graph management, including CRUD operations, batch block insertion, and full-text search.

AI assistant integration with Logseq knowledge graph: 21 tools to read, write, query, and search notes, enabling seamless interaction with your notes.

Enables AI assistants to interact with your local Logseq knowledge base through advanced search, content creation, template management, and knowledge organization with privacy-first, local-only operations.

A Model Context Protocol server that provides AI assistants with structured access to your Logseq knowledge graph, enabling retrieval, searching, analysis, and creation of content within your personal knowledge base.

An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.

Provides a SQLite-backed MCP server that records agent memory as history, enabling explicit consolidation, source checks, revisions, narrative versions, and accountable forgetting.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.