obsidian-mcp-context

Turn generated Obsidian-style vault notes into AI-ready context exposed through MCP, backed by Postgres and dbt marts.

This project uses generated/synthetic vault fixtures only. Do not use Gavin's personal Obsidian vault for this workflow.

Architecture

The supported main-repo workflow is:

completed generated Obsidian vault
  -> container-mounted vault at /vault
  -> Postgres raw landing tables
  -> dbt Postgres marts
  -> MCP consumers

The generator repository owns dataset creation and D3 growth visualization. This repository owns the deterministic batch ingest, dbt transformation, inspection surfaces, and MCP serving after a completed vault is handed over manually.

Postgres is the canonical warehouse. DuckDB is not part of the supported project workflow.

Related MCP server: Obsidian MCP Server

Features

• Generated realistic demo vaults under examples/generated-vaults (small, medium, and large).

• Deterministic parsing of headings, blocks, tasks, wikilinks, tags, semantic lines, and frontmatter.

• Postgres/dbt marts for entities, relationships, states, events, timelines, decisions, risks, and open loops.

• MCP tools for mart-backed context retrieval and direct parser diagnostics.

• Containerized Postgres, dbt, dbt Docs, table browser, and MCP services.

• Privacy posture that keeps personal vaults out of the project workflow.

Quickstart

The primary demo is a two-act flow: generate and visualize the dataset in the generator repo with D3, then manually hand the completed vault to this repo for the quiet Postgres/dbt/MCP workflow. See docs/demo-workflow.md for the full runbook.

Install the project:

git clone https://github.com/gavinsomers/obsidian-mcp-context.git
cd obsidian-mcp-context
python3 -m venv .venv
.venv/bin/python -m pip install -e ".[dev,pipeline]"

Run the quiet completed-dataset workflow when you want to ingest a completed vault, build/test dbt marts, and start MCP without Obsidian or replay services:

VAULT_PATH=./var/imported-vaults/generated-current \
  docker compose --profile workflow -f docker-compose.analytics.yml run --rm dataset-workflow

For checked-in fixture shortcuts, the host helper script is still available:

scripts/run_dataset_workflow.sh small
scripts/run_dataset_workflow.sh medium
scripts/run_dataset_workflow.sh large

This validates the selected dataset, starts Postgres, ingests the full vault, runs dbt, runs dbt tests, and starts MCP at http://localhost:8000. It does not copy data from the generator; pass the generated vault path explicitly after manually importing or placing it where you want it.

For the manual generator-to-main handoff rules, see docs/dataset-handoff-contract.md.

Start lineage and table inspection views only when you want to show proof:

VAULT_PATH=./var/imported-vaults/generated-current WITH_INSPECTION=1 \
  docker compose --profile workflow -f docker-compose.analytics.yml run --rm dataset-workflow

That also opens dbt Docs at http://localhost:8081 and the Postgres table browser at http://localhost:8082. Use --with-dbt-docs or --with-table-browser to start only one inspection surface.

Use your MCP client as the primary Q&A surface once the workflow has passed. Parser diagnostic commands remain available for source inspection, and dbt Docs or the table browser can be started explicitly when you need lineage or row-level evidence.

For the current representative prompt set and demo caveats, see docs/retrieval-validation.md.

Prove the same data is available through an agent-ready preset:

POSTGRES_DSN=postgresql://obsidian:obsidian@localhost:5432/obsidian_context \
POSTGRES_WAREHOUSE_SCHEMAS=mart,fact,dim,intermediate,staging \
.venv/bin/obsidian-mcp-context \
  --vault examples/generated-vaults/large \
  context-preset project_brief \
  --entity "Project Atlas 1" \
  --limit 5

The output includes mode: "mart-backed", the preset name, filters, row count, and source-linked rows from the dbt marts.

List the preset catalogue:

.venv/bin/obsidian-mcp-context context-presets

Legacy replay scripts still exist for old virtual-time experiments, but replay, Replay Q&A, and replay dashboards are no longer part of the main workflow.

The container MCP endpoint is:

http://localhost:8000

Serve dbt lineage and model documentation after building the warehouse:

scripts/run_dataset_workflow.sh large --with-dbt-docs

Then open:

http://localhost:8081

Inspect live Postgres raw tables and dbt marts in the browser:

scripts/run_dataset_workflow.sh large --with-table-browser

Then open http://localhost:8082 and log in to Adminer with server postgres, database obsidian_context, username obsidian, and password obsidian. The raw landing tables are in raw; dbt outputs are split across staging, intermediate, dim, fact, and mart.

For MCP client configuration, see docs/mcp-client-setup.md.

Generated Fixtures

The generated-large fixture includes companies, people, projects, decisions, risks, meetings, daily notes, research notes, tasks, links, tags, and lifecycle timestamps.

MCP Tools

Parser diagnostic tools read parsed Markdown directly:

• list_vault_notes

• search_vault_blocks

• list_vault_tasks

• get_vault_note_context

Mart-backed tools read dbt-built Postgres marts:

• list_vault_context_presets

• get_vault_context_preset

• get_vault_warehouse_summary

• get_vault_profile_metadata

• list_vault_entity_types

• get_vault_entity_context

• list_vault_entity_events

• list_vault_entity_relationships

• list_vault_entity_states

• list_vault_entity_open_loops

• get_vault_project_context

• get_vault_person_context

• list_vault_open_loops

• list_vault_decisions

• list_vault_risks

Use get_vault_context_preset as the normal agent-facing entry point when a named bundle such as project_brief, entity_brief, decision_log, or risk_register fits the task. Use lower-level mart tools when a client needs a specific table-shaped result. Parser tools are diagnostics for source inspection and troubleshooting.

Useful Docs

• Containerized analytics stack

• Demo workflow

• Manual dataset handoff contract

• MCP client setup

• Architecture

• Entity contract

• Stale context signals

• Configuration

• Vault profiler

• Doctor readiness report

• Generic core marts

• v1.0 release readiness

Verification

The main completed-dataset verification command is:

VAULT_PATH=./var/imported-vaults/generated-current WITH_INSPECTION=1 \
  docker compose --profile workflow -f docker-compose.analytics.yml run --rm dataset-workflow

It validates the completed vault, starts Postgres, ingests the dataset, runs dbt, runs dbt tests, starts MCP, and opens optional dbt lineage and table inspection surfaces.

Before recording a demo or preparing marketing screenshots, run the generated demo workflow and full privacy scan:

VAULT_PATH=./var/imported-vaults/generated-current WITH_INSPECTION=1 \
  docker compose --profile workflow -f docker-compose.analytics.yml run --rm dataset-workflow
scripts/privacy_check.sh --all

The privacy scan checks tracked files for blocked runtime artifacts and local sensitive terms from .privacy-banned-terms.local when that local-only file is present.