"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.

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.

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.

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.

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.

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.

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.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

Maps shell commands to standard MCP tools using single-file YAML specs.

Enables chat with Claude AI via terminal, supporting document retrieval, command-based prompts, and extensible tools through the Model Context Protocol.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

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

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