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GopikChenth

Mimir's Vault

by GopikChenth

Python MCP Protocol FastMCP Obsidian License


Key PillarsSystem ArchitectureInstallationMCP Client SetupTool ReferenceAgent Workflow Guide


The Problem: Wandering AI Coding Agents

When AI coding agents (Claude Code, Cursor, Antigravity) work on complex software repositories, they frequently suffer from Context Inflation & Wandering:

  • They blindly grep directories, re-scan full codebases, and load 30+ unrelated files into memory.

  • They waste thousands of LLM tokens, increase execution latency, and lose track of core architectural rules.


Related MCP server: knitbrain

The Solution: Mímir's Vault Memory Graph

Mímir's Vault (Mimirs_Vault) is a high-performance Model Context Protocol (MCP) server built in Python (FastMCP).

It maintains a human-readable, interlinked Obsidian-Wikilink memory graph inside ./.context/ for any software repository.

AI agents follow the Memory-First Scanning Rule:

  1. Inspect .context/overview.md via get_root_node.

  2. Follow Obsidian wikilinks ([[modules/auth]]).

  3. Inspect only the targeted source code files mapped in that module note.

  4. Edit zero unnecessary files.


Key Pillars

Rune

Pillar

Rationale

Memory-First Protocol

Mandates AI agents to consult .context/overview.md before reading project source code.

Obsidian-Wikilink Graph

Interlinked notes ([[modules/auth]]) viewable natively in Obsidian, VS Code, and Foam graph visualizers.

Zero Token Waste

Reduces context consumption by up to 80%, eliminating agent codebase wandering.

⚔️

Surgical Node Updates

Tools to append, update, and log Architectural Decision Records (ADRs) without clobbering existing notes.

Human & Agent Shared Memory

Clean Markdown files (.context/) readable by both human developers and LLMs alike.


System Architecture

<your-project-root>/
└── .context/                        <-- Memory Graph Root (Ignored in .gitignore)
    ├── overview.md                  <-- ROOT NODE (Scope, File Structure Map, [[modules/*]] index)
    ├── decisions.md                 <-- Architectural Decision Records (ADRs)
    └── modules/                     <-- LINKED MODULE NODES
        ├── auth.md                  <-- Module Note (Scope, Mapped Source Files)
        ├── database.md              <-- Module Note
        └── ui/                      <-- Submodule Directory
            └── components.md        <-- Submodule Note ([[modules/ui/components]])

Installation

Prerequisites

  • Python 3.10+

1. Clone the Repository & Initialize Environment

# Clone repository
git clone https://github.com/GopikChenth/Mimir-s_Vault.git
cd "Mimir-s_Vault"

# Create virtual environment & install dependencies
python3 -m venv .venv
.venv/bin/pip install "fastmcp>=3.4.0" "mcp>=1.2.0" "pydantic>=2.0.0"

2. Verify Installation & Test Graph Engine

.venv/bin/python test_graph.py

Expected Result: ALL TESTS PASSED SUCCESSFULLY!


MCP Client Setup

Add Mimirs_Vault to your preferred AI Client configuration:

Google Antigravity IDE (agy.json / Settings) or Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "Mimirs_Vault": {
      "command": "<path-to-repo>/.venv/bin/python",
      "args": [
        "<path-to-repo>/main.py"
      ]
    }
  }
}

Note: Replace <path-to-repo> with the absolute path where you cloned Mimir-s_Vault (e.g. /home/user/projects/Mimir-s_Vault or C:/Projects/Mimir-s_Vault).

Cursor IDE

  1. Open Cursor SettingsFeaturesMCP Servers.

  2. Click + Add New MCP Server.

  3. Name: Mimirs_Vault

  4. Type: command

  5. Command: <path-to-repo>/.venv/bin/python "<path-to-repo>/main.py"


MCP Tool Reference

Mimirs_Vault exposes 7 core tools for AI agent navigation:

Tool

Signature

Purpose

get_root_node

(workspace_path: str = ".")

Fetches .context/overview.md (Project Scope, Master File Structure Map, [[modules/*]] index).

navigate_node

(node_path: str, workspace_path: str = ".")

Traverses a wikilink node (e.g. modules/auth, decisions). Returns content & mapped source files.

search_nodes

(query: str, workspace_path: str = ".")

Performs keyword and topic search across all .context/ markdown notes.

init_context_graph

(project_name, description, scope, workspace_path)

Bootstraps .context/ folder with starter overview.md and decisions.md.

create_module_node

(module_name, scope, source_files, parent_node)

Creates a module note and registers [[modules/name]] in the parent node.

update_node

(node_path, content, section_heading, mode)

Surgically appends or replaces content under a specific section heading.

log_decision

(title, context, decision, consequences)

Appends an Architectural Decision Record (ADR) in .context/decisions.md.


System Prompts & Resources

MCP Prompts

  • memory_first_workflow: Instructs AI agents: "Consult get_root_node first. Read ONLY the target source files mapped in module wikilinks."

  • bootstrap_project_context: Guides AI to auto-scan an un-documented repository and build .context/ notes.

MCP Resources

  • context://root — Direct read of .context/overview.md.

  • context://decisions — Direct read of .context/decisions.md.


Agent Workflow Guide

1. Bootstrapping a New Repository

In any project workspace, prompt your AI agent:

"Run the bootstrap_project_context prompt to initialize the memory graph for this repository."

The AI will call init_context_graph and create_module_node to build your project's .context/ tree.

2. Daily Task Execution

When asking your AI agent to implement a feature:

"Implement user authentication with JWT. Check the memory graph first."

The AI will:

  1. Call get_root_node to inspect .context/overview.md.

  2. Follow [[modules/auth]] to extract mapped file paths (src/auth/service.py).

  3. Inspect & edit only src/auth/service.py (zero token waste).

3. Logging Architecture Decisions

"Log an architectural decision that we switched to RS256 JWT signing."

The AI calls log_decision, appending ADR-002 to .context/decisions.md.


Visualizing Your Memory Graph in 3D

Because all nodes use standard Obsidian Wikilink syntax ([[modules/auth]]), open your repository's .context/ directory in:

Enjoy an interactive, 3D visual graph of your codebase's architectural memory!


ᛗ Built with Wisdom for AI-First Engineering ᛗ

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