llm-wiki-mcp

llm-wiki-mcp

Persistent markdown wiki for your AI agent, built on Karpathy's LLM wiki gist.

Category
Visit Server

README

llm-wiki-mcp

Persistent markdown wiki for your AI agent, built on Karpathy's LLM wiki gist. Four MCP tools (wiki_read, wiki_write_page, wiki_log_append, wiki_inventory) plus four Claude Code skills (wiki-init, wiki-ingest, wiki-query, wiki-lint). stdio transport, local filesystem.

The server handles the boring layer LLMs keep getting wrong: atomic writes, etag conflict checks, append-only log integrity, path containment. The skills give the agent a workflow to follow. The wiki schema lives in your own wiki/CLAUDE.md and grows with your domain. There is no Layer 3 schema validation in the server.

Status: alpha (v0.1.1). Local backend only. MIT licensed.

Quick start

Requires Python 3.11+ and uv.

Pick an absolute path for the wiki folder. The server creates pages/ and log.md under it on first run if they don't exist:

uvx llm-wiki-mcp --wiki-root /absolute/path/to/wiki

Wire it into your MCP client.

Claude Code:

claude mcp add llm-wiki -- uvx llm-wiki-mcp --wiki-root /absolute/path/to/wiki

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS) or Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "llm-wiki": {
      "command": "uvx",
      "args": ["llm-wiki-mcp", "--wiki-root", "/absolute/path/to/wiki"]
    }
  }
}

Restart the client. Four tools should appear: wiki_read, wiki_write_page, wiki_log_append, wiki_inventory.

Claude Code skills

Claude Code users can install the bundled workflow skills as a plugin:

claude plugin marketplace add https://github.com/flsteven87/llm-wiki-mcp
claude plugin install llm-wiki-skills@llm-wiki-mcp

Each skill reads wiki/CLAUDE.md for the active schema on every run, so you can evolve the schema without re-installing anything. Ask the agent things like:

Skill What to ask Needs MCP server?
wiki-init "Create an LLM wiki for AI safety research at ~/wikis/ai-safety." No
wiki-ingest "Ingest https://arxiv.org/abs/2310.12345 into the wiki." Yes
wiki-query "What does the wiki say about steering vectors?" Yes
wiki-lint "Run a wiki health check." Yes

wiki-init is a one-shot scaffolder; the other three are Karpathy's three operations.

Other MCP clients (Claude Desktop, Cursor) get the four tools but not the skills. The agent has to derive the workflow from tool descriptions alone, which works for one-off reads and writes but tends to skip the bookkeeping (log entries, backlink audits) the skills make explicit.

The four tools

Tool Annotations Purpose
wiki_read read-only, idempotent Read one page. Returns body, parsed frontmatter, outgoing links, etag.
wiki_write_page destructive, idempotent Atomic create or update with etag CAS. Pass etag=null to create, the read etag to update.
wiki_log_append not idempotent Append one entry to log.md in Karpathy's ## [YYYY-MM-DD] op | Title format.
wiki_inventory read-only, idempotent Snapshot the whole graph: pages, frontmatter, link edges, log entries, plus an optional plain-text mention scan for backlink audits.

index.md and raw/ are intentionally not exposed as tools. The index is LLM-curated content edited via the host's Read/Write. The raw layer is immutable from the server's perspective.

Wiki layout

wiki-init scaffolds a project that looks like this:

your-project/
├── raw/                    Immutable source files (papers, articles, transcripts)
│   └── ...
└── wiki/                   ← --wiki-root points here
    ├── pages/              Markdown pages, one per topic
    ├── log.md              Append-only session log
    ├── index.md            LLM-curated browse page
    └── CLAUDE.md           Schema doc the LLM reads on every operation

--wiki-root points at the curated wiki/ folder, not the parent project folder containing raw/. Easy to get wrong on first install; the troubleshooting section below covers the error you'll see.

Design boundary

The server enforces mechanics, not content shape:

  • Atomic writes. tmp-file + fsync + rename for pages. O_APPEND single-write for log entries.
  • Optimistic concurrency. Every page has an etag (sha256(body) || mtime_ns). Updates supply the etag they read; a mismatch raises WikiConflictError, and the agent re-reads, merges, and retries.
  • Path containment. Slugs are regex-validated. Resolved paths are checked against the realpath of the root, blocking the CVE-2025-53109 symlink-escape class.
  • Format-locked log line. ## [YYYY-MM-DD] operation | Title. Operation names are free strings; only characters that would break the line shape are rejected.

The server does not validate frontmatter shape, page categories, or link targets. That layer lives in your wiki/CLAUDE.md schema doc and grows with the LLM. Karpathy's gist is deliberately silent on content shape; baking a schema into the server would defeat the point.

Python API

If you want to wrap the MCP server with your own storage backend (SQLite, Notion, GDrive, a test fake), implement the WikiStorage Protocol and pass an instance to build_server:

from llm_wiki_mcp import WikiStorage, PageRead, LogEntry
from llm_wiki_mcp.server import build_server

class MyStorage:  # satisfies the WikiStorage Protocol
    async def read_page(self, slug: str) -> PageRead: ...
    async def write_page(self, slug, body, expected_etag=None) -> str: ...
    async def list_pages(self) -> list[str]: ...
    async def append_log(self, entry: LogEntry) -> None: ...
    async def read_log(self) -> str: ...
    async def write_raw_file(self, name, data) -> None: ...  # usually raises

server = build_server(storage=MyStorage())
server.run()

build_server is the composition root. The CLI main() is a thin caller that constructs LocalFilesystemStorage from --wiki-root and hands it in.

The bundled Claude Code skills ship as package data under llm_wiki_mcp/skills/ and load via importlib.resources if you want to wire them into a non-Claude-Code agent. Typed domain errors (WikiConflictError, WikiNotFoundError, WikiPermissionError, WikiPathError, WikiSchemaViolationError) are importable from the package root for catching at your own boundary.

Troubleshooting

llm-wiki-mcp: command not found after uv tool install. uv puts the binary in ~/.local/bin (or %USERPROFILE%\.local\bin on Windows). Add it to PATH, or use uvx llm-wiki-mcp ... to invoke without a persistent shim.

wiki_* tools don't appear after editing the client config. Restart the MCP client. Claude Desktop, Claude Code, and Cursor only re-read mcpServers at startup.

WikiPathError: path escapes wiki root. You pointed --wiki-root at the project folder containing raw/ instead of the curated wiki/ folder inside it. /Users/me/wikis/ai-safety/wiki is correct; /Users/me/wikis/ai-safety is not.

Skills not loading in Claude Code. Run claude plugin list. If llm-wiki-skills is missing, rerun the marketplace commands in the Claude Code skills section.

Development

git clone https://github.com/flsteven87/llm-wiki-mcp
cd llm-wiki-mcp
uv sync --extra dev
uv run pytest
uv run ruff check .
uv run pyright src/llm_wiki_mcp

License

MIT. See LICENSE.

<!-- mcp-name: io.github.flsteven87/llm-wiki-mcp -->

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured