obsidian-mcp: Graph-Aware MCP Server for Claude with 25 Tools Targeting Large Vaults
Built by u/One-Classroom-9261, obsidian-mcp is an MCP server that gives Claude graph-aware access to your Obsidian vault. Unlike most integrations that only expose read_file and write_file — which forces the model to make ~50 read calls to answer a simple connection question and fills your context window — this server exposes the vault's graph structure directly.
Key Tools
get_note— returns content + backlinks + forward links + tags + frontmatter in a single calltraverse_graph— walk N hops out from a notequery_dataview— run DQL (Dataview Query Language) directlymove_note— rename + rewrite every incoming wiki-link so the graph survivescreate_notes— batch-scaffold a Map of Content (MOC) plus topical notes in one shot
Total: 25 tools. Compatible with Claude Desktop, Claude Code, Cursor, Cline, Continue, and Zed.
Setup
If you already have the Local REST API plugin installed in Obsidian, setup takes about 60 seconds. The project is MIT licensed, runs locally, and nothing leaves your machine.
The author built it in ~24 hours, notes there are rough edges, and welcomes feedback.
📖 Read the full source: r/ClaudeAI
👀 See Also
Automated Claude Code Pipeline Cuts Token Usage from 78k to 15k Per Feature
An open-source pipeline for Claude Code automates 12 phases including pre-check analysis of existing code, reducing token usage from ~78k to ~15k per feature. It offers three profiles (yolo, standard, paranoid) and replaces confidence scores with grep-based validation.
ai-codex: Pre-index your codebase to save Claude tokens
ai-codex is a tool that generates compact markdown indexes of your codebase, allowing Claude Code to skip the initial exploration phase that typically consumes 30-50K tokens per conversation. It creates five files covering routes, pages, libraries, schemas, and components.
Codesight: AI Context Engine Cuts 30K-60K Tokens from Claude Code Sessions
Codesight is an open-source tool that analyzes codebases to provide AI coding agents with structured context, reducing token waste. A developer collaborated with the maintainer to add AST parsing for Next.js and Prisma, an eval suite, token telemetry, and profiles for Claude Code and Cursor.
Practical Findings from 11 Multi-Agent Software Builds Without Programmatic Scaffolding
Analysis of 11 autonomous multi-agent builds shows scope enforcement works mechanically (20/20 success) not via prompts (0/20), orchestration costs are dominated by memory re-ingestion (~95% of input spend), and worker model capability creates 9.8x throughput gaps.