personal-context-mcp
A portable, Markdown-based memory system for Claude Desktop that separates durable context, working memory, and low-trust notes so AI assistants can stay useful without trapping memory inside one product.
README
Personal Context MCP
A Markdown-first MCP for Claude Desktop that makes AI memory portable, structured, and user-owned.
What if Claude could wake up inside a memory system you actually own?
personal-context-mcp gives Claude a local folder of Markdown context: durable truths, live priorities, rough notes, and reviewable updates. Claude can search it, build a focused brief before work starts, and capture new observations without turning them into permanent truth by accident.
Instead of trusting an opaque app memory, you get a small personal context layer you can inspect, edit, version, copy, and take with you.
The 10-Second Version
Without this: every important chat starts with you re-explaining who you are, what matters, how you work, and what is currently going on.
With this: Claude can run wake_up_context, read the right local Markdown sections first, and start the task with the context that matters.
In Action

The cool part is the loop: search what matters, wake up with the right bundle, capture rough notes safely, then promote only the pieces that earn trust.
Want the product shape in five minutes? Read the V2 demo walkthrough.
Why Use It
This is most useful if you:
- use Claude heavily
- switch between multiple AI workflows
- want continuity across sessions and projects
- care about user-owned memory instead of product-owned memory lock-in
- want important context to stay reviewable in plain files
What Makes This Different
This project is intentionally opinionated:
- Markdown-first, so the memory layer stays portable and inspectable
core / dynamic / inbox, so durable truth, working memory, and tentative notes do not get mixed together- human-reviewable, so important context is visible and editable in plain files
- trust-aware, so rough notes can be captured without pretending they are durable truth
- local by default, so your filled portfolio stays under your control
The point is not "more memory." It is a more inspectable path from rough context to useful context, while keeping durable truth protected.
That is the core bet: the best AI memory is not invisible. It is readable, local, and curated.
V2 Surface
The current V2 surface turns the project from a basic Markdown memory bridge into a fuller personal-context workflow:
- Ranked search:
search_memoryfinds relevant files and sections with transparent ranking reasons. - Wake-up bundles:
wake_up_contextbuilds a small trust-aware bundle before a task starts, so Claude can read the right context first. - Durable context wrappers:
writing_style_context,product_positioning_context, andoutbound_framing_contextgive common high-value prompts explicit entry points. - Manual ingestion:
manual_ingestcaptures pasted notes, transcript excerpts, and rough summaries into low-trust inbox memory with visible provenance. - Review and promotion:
promote_raw_note,mark_raw_note_status, andlink_raw_note_to_proposallet rough notes move toward reusable memory without erasing their trail. - Safer dynamic maintenance: dynamic files can be appended to, bootstrapped, or updated through exact section/item operations instead of broad rewrites.
The upgrade is not just more tools. It is a tighter loop:
- Ask Claude what context exists.
- Search and rank the right Markdown sections.
- Build a focused read-first bundle.
- Capture rough notes into low-trust inbox memory.
- Promote only reviewed context into reusable working memory.
Memory Model
This project uses a simple three-layer memory model:
core/Durable truth. Identity, positioning, communication style, operating rules.dynamic/Working memory. Current priorities, active campaigns, recent learnings, message tests, account patterns.inbox/Low-trust notes. Rough observations, partial ideas, and proposed core updates.
This separation is the key design choice: stable truth, evolving memory, and uncertain notes should not collapse into one blob.
Tool Surface
| Tool | Surface | Purpose |
|---|---|---|
status |
read-only | Show memory health, writable surfaces, and available tools. |
list_memory_files |
read-only | List portfolio files Claude can inspect. |
read_memory_file |
read-only | Read one specific memory file. |
search_memory |
read-only | Search files and sections with ranked matches and ranking reasons. |
wake_up_context |
read-only | Build a compact, trust-aware context bundle for a task. |
writing_style_context |
read-only | Pull writing-style context for drafting, rewriting, and editing tasks. |
product_positioning_context |
read-only | Pull product and positioning context for strategy or messaging work. |
outbound_framing_context |
read-only | Pull outbound framing context for prospecting and outreach work. |
manual_ingest |
inbox/ write |
Capture pasted notes into low-trust memory with provenance. |
append_memory_entry |
dynamic/ write |
Append a durable learning to an approved dynamic section. |
maintain_dynamic_item |
dynamic/ write |
Replace or remove one exact bullet or dated entry. |
replace_dynamic_section |
dynamic/ write |
Replace one approved dynamic section. |
bootstrap_dynamic_memory |
dynamic/ write |
Bootstrap dynamic memory from reusable conversation evidence. |
mark_raw_note_status |
inbox/ write |
Mark raw notes reviewed or promoted without deleting the source note. |
promote_raw_note |
dynamic/ or inbox/ write |
Promote one reviewed raw note into a learning or core-update proposal. |
link_raw_note_to_proposal |
inbox/ write |
Link a raw note to an existing core-update proposal. |
propose_core_update |
inbox/ write |
Propose protected core changes without editing core/ directly. |
Privacy Boundary
The starter portfolio in this repo is intentionally blank and safe to copy. Your filled personal context portfolio is private data.
Do not commit your filled portfolio to a public repo. The default quickstart name, my-personal-context-portfolio/, is ignored by this repo, but if you choose another name, keep it outside Git or add it to your own .gitignore before filling it in.
See PRIVACY.md for the privacy model.
Requirements
- Claude Desktop with local extensions enabled
- Node.js 20 or newer
- npm
Install
See QUICKSTART.md.
git clone https://github.com/abhinavkalyan10/personal-context-mcp.git
cd personal-context-mcp
npm install
npm run install:extension
npm run prepare:extension
Then install .build/claude-extension/personal-context as an unpacked extension in Claude Desktop.
Repo Structure
.
├── assets/
├── desktop-extension/personal-context/
├── lib/
├── mcp/
├── scripts/
├── starter-personal-context-portfolio/
├── tests/
├── QUICKSTART.md
└── package.json
What This Is Not
This is not magical autonomous memory.
It does not replace:
- judgment
- selective curation
- periodic cleanup
- manual review of durable truth
It also does not add embeddings, a vector database, a knowledge graph, or automatic background writes.
Project Docs
- QUICKSTART.md: install and validation walkthrough
- docs/v2-demo-walkthrough.md: short guided tour of the V2 workflow
- CHANGELOG.md: release history
- PRIVACY.md: privacy model and data boundaries
- SECURITY.md: security policy
Suggested One-Line Description
Personal Context MCP is a portable, Markdown-based memory system for Claude Desktop that separates durable context, working memory, and low-trust notes so AI assistants can stay useful without trapping memory inside one product.
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.