"A comparison or overview of Obsidian and Logseq" matching MCP servers:

Enables developers and product owners to scan a codebase and extract a readable business overview—features, roles, access control, data model, and endpoints—along with findings on missing or inconsistent rules, and generate shareable HTML or Markdown reports and diagrams.

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

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.

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

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 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.

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

Interacts with LogSeq via its API.

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.

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

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.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.