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Obsidian MCP Learning System

A local MCP server that transforms an Obsidian vault into a structured learning interface through tools for concept extraction and gap analysis. It enables AI agents to generate study plans and align note-taking with implementation projects for a more cohesive learning workflow.

glama
Updated
Mar 25, 2026
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dhruv's obsidian mcp

i'm working on learning ML compilers + some os concepts this summer so i set up an obsidian vault to track my learning

coincidentally i also wanted some more experience working with mcp so im gonna create an mcp server that connects to codex and gives it some pointers on how to evaluate my learning + make sure my projects are on the right track

stay tuned for updates! we're just getting started ☺︎

Roadmap

This project is a local MCP server for turning an Obsidian vault into a structured compiler-learning interface.

The goal is not just "AI can read my notes."
The goal is to build a real MCP-native learning system with:

  • tools for computation and analysis
  • resources for stable context surfaces
  • prompts for reusable workflows

Current State

Phase 1 — Existing MCP integration

  • Connect Codex to an existing Obsidian MCP server
  • Validate basic vault access and note summarization

Phase 2 — Custom local MCP server

  • Build a custom Python MCP server
  • Expose initial tools:
    • extract_concepts
    • get_learning_gaps
    • generate_study_session
    • compare_notes_to_project
  • Register server in Codex
  • Verify with MCP Inspector
  • Get working end-to-end local tool calls

Next Up

Phase 3 — Flesh out the MCP interface

Goal: evolve from "bag of tools" into a real MCP-backed learning interface.

3.1 Resources

Expose stable, inspectable views of the learning system.

Planned resources:

  • vault://compiler/concepts
  • vault://compiler/gaps
  • vault://compiler/recent-notes
  • vault://project/alignment
  • vault://weekly-review/latest

Why:

  • tools are good for actions
  • resources are good for persistent context
  • this makes the server feel more like a system and less like one-off functions

3.2 Prompts

Add reusable workflow templates directly through MCP.

Planned prompts:

  • weekly_learning_review
  • generate_study_plan
  • notes_vs_project_analysis
  • paper_to_implementation_breakdown

Why:

  • removes the need to remember good prompt phrasing
  • turns repeated workflows into first-class interfaces

3.3 Better analysis heuristics

Upgrade from raw keyword counting to more meaningful note analysis.

Planned improvements:

  • frontmatter-aware filtering
  • tag-aware concept grouping
  • recency-aware analysis
  • note depth scoring
  • backlinks / note-link graph analysis
  • better "shallow vs deep" detection
  • concept clustering instead of only exact keyword hits

Why:

  • current heuristics are useful but primitive
  • this is where the actual intelligence of the server improves

Phase 4 — Multi-source learning system

Goal: compare and synthesize across more than just markdown notes.

Planned sources:

  • Obsidian vault
  • local project repo(s)
  • paper notes / reading notes
  • PDFs or exported paper summaries
  • lightweight project tracker / task file

Planned capabilities:

  • compare notes to implementation
  • compare paper concepts to project gaps
  • detect studied-but-not-built topics
  • generate implementation ideas from recent learning

Why:

  • this is where MCP starts becoming genuinely high leverage
  • the server becomes a bridge between learning, planning, and building

Phase 5 — Codex workflow integration

Goal: make the server easy and natural to use inside daily Codex workflows.

Planned work:

  • improve tool naming and descriptions
  • make outputs more structured and predictable
  • add AGENTS.md guidance for when to use each MCP feature
  • add example prompts for each tool/resource/prompt
  • reduce need for manual tool invocation phrasing

Why:

  • a powerful MCP server is useless if the host/client doesn’t use it well
  • ergonomics matter just as much as capabilities

Future / Stretch Ideas

Sampling

Potential future direction:

  • let the server request model-generated synthesis through MCP sampling

Possible use cases:

  • auto-generate weekly reviews
  • synthesize study guides from grouped notes
  • produce concept summaries from note clusters

Note: This is intentionally not a near-term priority.
The server should first have strong tools/resources/prompts before adding more agentic behavior.

Remote / hosted version

Potential future direction:

  • move from local stdio server to remote server
  • support HTTP transport
  • add auth if needed
  • support broader clients beyond local Codex usage

Note: This is productization, not the immediate learning goal.

Immediate Priorities

Priority 1

Implement resources:

  • concepts
  • gaps
  • recent notes digest
  • notes/project alignment summary

Priority 2

Implement prompts:

  • weekly review
  • study session
  • notes vs project comparison

Priority 3

Improve heuristics:

  • frontmatter and tag support
  • recency filters
  • better depth scoring

Guiding Principle

This project should move toward:

a real MCP interface for a compiler-learning workflow

and away from:

a pile of loosely related note-analysis functions

If a new feature does not improve one of these, it probably should not be added:

  • learning feedback loops
  • study planning
  • notes-to-project alignment
  • reusable Codex workflows
  • structured MCP-native interfaces

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