Hivemind

Hivemind is an Obsidian plugin that provides AI firewall capabilities for fictional world - building, research, and knowledge management. It ensures that AI tools collaborate based on the real information in the user's notes through timeline views, relationship graphs, and canonical workflows, preventing AI hallucinations.

Knowledge management and memory Note-taking tools #AI firewall #Knowledge management #Obsidian plugin #World - building .TypeScript

rating : 2.5 points

downloads : 3.4K

update time : 2026-03-12

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What is the Hivemind MCP Server?

The Hivemind MCP Server is a bridge that connects your Obsidian knowledge base with AI assistants. It adheres to the Model Context Protocol standard, allowing AI tools (such as Claude Desktop and GitHub Copilot) to directly refer to the real content in your Obsidian notes when answering your questions, rather than relying on the AI's memory or potentially hallucinated information. In simple terms, it turns the AI into an 'intelligent search engine' for your knowledge base, ensuring that all responses are based on the facts and data you've confirmed.

How to use the Hivemind MCP Server?

Using the Hivemind MCP Server is very simple: 1. Install and configure the Hivemind plugin in Obsidian. 2. Add the MCP server configuration to your AI client (such as Claude Desktop). 3. After starting the server, the AI assistant can access your knowledge base. 4. In AI conversations, you can ask questions about the content of your notes, and the AI will answer based on real - world data. The entire process runs locally, and your data will not leave your device.

Use cases

The Hivemind MCP Server is particularly suitable for the following scenarios: • Novel writing and world - building: The AI provides creative assistance based on the character settings, timelines, and worldviews you've established. • Academic research and knowledge management: The AI accurately quotes the research papers, notes, and concepts you've collected. • Team management and project planning: The AI provides suggestions by referring to meeting records, personnel profiles, and project documents. • Software development: The AI provides code suggestions based on your architecture decision records (ADRs) and system designs. It is applicable to any scenario where the AI needs to work based on an accurate and consistent knowledge base.

Main features

Secure local data access

The MCP server runs entirely locally, and your Obsidian note data will never leave your device. The AI queries data through a secure local connection, ensuring privacy and security.

Support for multiple AI clients

Supports all AI clients compatible with the MCP protocol, including Claude Desktop, GitHub Copilot, Cursor, etc. Configure once and use in multiple places.

Intelligent entity recognition

Automatically identifies the entity types (people, places, events, etc.) in the notes, enabling the AI to understand the structure of your knowledge base and conduct more accurate queries.

Template - based knowledge structure

Provides predefined templates (world - building, research, personnel management, etc.) to help you standardize the note structure, making it easier for the AI to understand and query.

Canon workflow integration

The AI can distinguish between draft, pending - review, and confirmed content, ensuring that responses are based on the 'canonical' information you've confirmed and avoiding the citation of unverified content.

Natural language query

Use natural language to ask questions about your knowledge base, such as 'Show all notes related to the magic system' or 'Find all the allies of the protagonist'.

Advantages

Eliminate AI hallucinations: Ensure that the AI's responses are based on your real data, rather than fictional content.

Maintain consistency: The AI always quotes the latest and confirmed note content.

Improve efficiency: Quickly extract relevant information from a large number of notes without manual searching.

Protect privacy: All data processing is done locally, and the data does not leave the device.

Easy to integrate: Seamlessly integrate with the existing Obsidian workflow without changing your note - taking habits.

Cross - platform support: Supports Windows, macOS, and Linux systems.

Limitations

Requires configuration: Initial use requires client configuration.

Depends on note quality: The quality of the AI's responses depends on the structure and integrity of your notes.

Text - only: Currently mainly processes text content, with limited support for multimedia such as images.

Requires Obsidian: Must use Obsidian as the knowledge management tool.

Occupies local resources: Running the MCP server requires a certain amount of system resources.

How to use

Install the Hivemind Obsidian plugin

Install the Hivemind plugin in Obsidian and select a suitable template (world - building, research, etc.).

Configure the AI client

Add the MCP server configuration according to the AI client you're using. Here is an example of the configuration for Claude Desktop.

Start the MCP server

Ensure that the Hivemind plugin is enabled, and the MCP server will start automatically. You can check the server status in Obsidian.

Start a conversation with the AI

In the AI client, you can now ask questions about your knowledge base. The AI will automatically query your notes to answer the questions.

Usage examples

Novel writing assistance

When writing a novel, you need to ensure that the AI assistant provides suggestions based on the character settings and worldviews you've established, avoiding contradictions.

Research paper organization

When writing a research paper, you need the AI to accurately quote the literature and notes you've collected, rather than relying on general knowledge.

Project management

When managing a project, you need the AI to provide status updates and suggestions based on real meeting records and task assignments.

Frequently Asked Questions

Is the MCP server secure? Will my data be uploaded?

Do I need to change my existing note structure?

Which AI clients are supported?

What if the AI can't find relevant information?

Can I connect multiple knowledge bases simultaneously?

Do I need programming knowledge for configuration?

Related resources

Official documentation

Detailed MCP server configuration guide, including configuration examples for each platform

Obsidian plugin page

Obsidian community plugin page, view user reviews and update logs

Model Context Protocol official website

Official documentation and specifications of the MCP protocol

Example knowledge base

Example Obsidian vaults for various use cases

Problem feedback and discussion

GitHub discussion area, ask questions and share usage experiences

🚀 Hivemind Research Documentation Index

This documentation provides a comprehensive overview of the research and planning for Hivemind, an MCP server that bridges Obsidian and AI tools.

🚀 Quick Start

This directory is a treasure trove of research and planning documents for Hivemind, an MCP server. It serves as a bridge between Obsidian, where worldbuilders create, and Claude/AI tools, which require consistent context. It's important to note that Hivemind is not a replacement for either.

✨ Features

Key Insight

Hivemind acts as a bridge between Obsidian (the workspace for worldbuilders) and Claude/AI tools (which need consistent context), rather than a substitute for them.

📚 Documentation

1. 📋 FEATURES.md (40 KB - PRIMARY RESEARCH)

Comprehensive feature research document

It covers:

Core MCP Capabilities: Tools, resources, prompts, query patterns, and best practices.
Obsidian Vault Features: Folder structure, templates, metadata schemas, and relationship modeling.
AI Context Delivery: Task - specific context, character consistency, location/timeline context, and asset references.
Canon Management: Draft→approval workflow, version control, conflict resolution, and validation.
Asset Provenance: ComfyUI workflows, metadata tracking, and reproducibility.
Feature Prioritization: MVP features, v0.2+ nice - to - haves, v1.0+ future, and out - of - scope features.

When to read: For a complete understanding of system design and capabilities. Time to read: 30 - 45 minutes

2. 🎯 RESEARCH_SUMMARY.md (8 KB - QUICK REFERENCE)

Executive summary of key findings

It contains:

High - level findings for each major area.
Design decisions and rationale.
Risk assessment and mitigations.
Implementation roadmap phases.
Source validation.

When to read: To quickly understand what research validated and why. Time to read: 5 - 10 minutes

3. ✅ IMPLEMENTATION_CHECKLIST.md (12 KB - ACTIONABLE)

Phase - by - phase implementation guide

It contains:

MVP checklist (v0.1).
Phase 1 detailed tasks (Week 1 - 2).
Phase 2 expansion tasks (Week 3 - 4).
Phase 3 polish tasks (Week 5+).
Code patterns ready to copy.
Success criteria.

When to read: To understand what to build and in what order. Time to read: 10 - 15 minutes

4. 🏗️ ARCHITECTURE.md (In vault)

System design and component diagrams

See: .planning/research/ARCHITECTURE.md

5. 📚 STACK.md (In vault)

Technology choices and justification

See: .planning/research/STACK.md

Quick Navigation by Role

👨‍💻 Developer: "How do I build this?"

Start: RESEARCH_SUMMARY.md (understand scope).
Deep dive: FEATURES.md section "Core MCP Capabilities" + "Obsidian Vault Features".
Build: IMPLEMENTATION_CHECKLIST.md.
Reference: FEATURES.md section "Query Patterns" for API design.

🏛️ Architect: "What's the overall design?"

Overview: RESEARCH_SUMMARY.md.
Design details: ARCHITECTURE.md.
Feature scope: FEATURES.md section "Feature Prioritization".
Constraints: FEATURES.md section "Out of Scope".

🧪 QA: "What should I test?"

Scope: IMPLEMENTATION_CHECKLIST.md section "Testing & Validation".
Success criteria: IMPLEMENTATION_CHECKLIST.md.
Data formats: FEATURES.md section "Obsidian Vault Features".
Edge cases: FEATURES.md section "Risks & Mitigations".

📝 Product Manager: "What's the vision?"

Quick overview: RESEARCH_SUMMARY.md.
MVP scope: FEATURES.md section "Feature Prioritization" → MVP.
Roadmap: RESEARCH_SUMMARY.md section "Implementation Roadmap".
Risks: RESEARCH_SUMMARY.md section "Risks & Mitigations".

🎨 Designer: "What does the user experience look like?"

System overview: FEATURES.md "What MCP is".
User workflows: FEATURES.md section "Canon Management".
Data templates: FEATURES.md section "Note Types & Templates".
Context examples: FEATURES.md section "AI Context Delivery".

🔧 Technical Details

Key Research Findings (TL;DR)

✅ Validated

MCP can expose worldbuilding data without modifying Obsidian.
Obsidian YAML + wikilinks are sufficient for relationship modeling.
Claude's 200K token context window is sufficient for world context.
Existing tools (ComfyUI, Dataview) can be integrated without reimplementation.
Git - based approval workflow works for collaborative worldbuilding.

🎯 Design Decisions Made

Single source of truth: Obsidian vault (not external DB).
Architecture: MCP server as a read - only bridge.
Relationships: Bidirectional via wikilinks + YAML metadata.
Approval: Git - based (main = canon, features = proposals).
Assets: Embed metadata in images; version workflows.

📊 Feature Scope

MVP (v0.1): Basic queries, templates, manual approval.
v0.2 - 0.3: Filtered queries, consistency checks, ComfyUI integration.
v1.0+: Multi - world, web UI, collaboration features.

🚫 Out of Scope

Replace Obsidian (it's not an editor).
External storage (vault is the source of truth).
Arbitrary code execution.
Auto - generate complete worlds.
Real - time multiplayer editing.

How This Fits Together

User (Worldbuilder)
        ↓
Obsidian Vault (Source of Truth)
        ↓
Git Repository (Version Control)
        ↓
Hivemind MCP Server (Bridge)
        ↓
Claude / AI Tools (Context Consumers)

Data Flow:

Worldbuilder edits characters/locations in Obsidian.
Changes are committed to git.
Hivemind MCP server reads from git.
Claude queries MCP for context.
Claude generates consistent content (dialogue, images, plot).
Results go back to the worldbuilder.

Research Sources

Research was conducted in January 2025 using:

1000+ MCP server examples (MCPMarket.io).
5+ Obsidian worldbuilding vault templates.
Official MCP documentation (modelcontextprotocol.io).
Claude API docs (platform.claude.com).
ComfyUI workflow documentation.
Leading worldbuilding tools (World Anvil, Reality Forge, Urdr).

Implementation Timeline (Estimated)

Phase	Weeks	Focus	Deliverable
Research	1	Validation	✓ This directory
MVP	1 - 2	Core MCP server	Basic queries, templates
Expansion	3 - 4	Enhanced features	Consistency checking, ComfyUI
Polish	5+	Performance	Web UI, analytics

Success Metrics

MVP Success

✅ Claude can query character data.
✅ Context stays within token limits.
✅ Consistency checks work.
✅ Templates are easy to use.
✅ Approval workflow is functional.

v1.0 Success

✅ Multi - user collaborative worldbuilding.
✅ 1000+ entities are handled smoothly.
✅ Web interface for browsing.
✅ Full asset provenance tracking.
✅ Community adoption (external vaults).

Open Questions for Implementation

What MCP runtime? (TypeScript, Python, Rust?)
- Decision point: language expertise, deployment target.
How to handle vault changes? (git hooks, polling, file watcher?)
- Design point: latency vs. system load.
Where to cache? (in - process, Redis, persistent file?)
- Performance point: memory vs. responsiveness.
How to validate consistency? (strict rules or warnings?)
- UX point: helpful vs. annoying.
When to integrate ComfyUI? (v0.1, v0.2, v1.0?)
- Scope point: complexity vs. time to MVP.