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

Interacts with LogSeq via its API.

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.

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

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

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

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.

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.

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

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.

A NestJS-based server that enables AI agents to interact with Logseq graphs through its HTTP API for managing notes, journals, and blocks. It features specialized tools for project development tracking, including progress logs, technical decisions, and workflow prompts.

An MCP server that lets an AI agent operate a Logseq (DB) graph through safe, high-level tools for reading and managing notes, tasks, and projects without needing to know Logseq's internals.

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

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.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.

Enables querying normalized real-world asset data through MCP tools, including listing assets, retrieving asset details, comparing yields against SOFR, and checking eligibility by jurisdiction.

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

Turns Claude-style skill folders into MCP tools, resources, and prompts for discovering and using skills.

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.

Enables fetching weather forecasts for the USA via NWS API and for Israel via browser automation using Playwright.