llm-wiki-mcp
An MCP server that provides AI agents with a governed wiki requiring human approval before any formal page update, combining structured knowledge management with safe, auditable writes.
README
llm-wiki-mcp
An MCP server that gives your AI agents a governed wiki instead of unrestricted filesystem access. It sits between "full power" and "read-only" by requiring human approval before any formal wiki page gets updated.
Design philosophy draws from Karpathy's LLM Wiki methodology: compile knowledge into a reusable wiki during ingest/maintenance, rather than re-synthesizing from raw sources on every query. Astro-Han/karpathy-llm-wiki and multica-ai/andrej-karpathy-skills served as engineering references.
The project started by hand-crafting ~/llm-wiki to validate the workflow, then abstracting it into an MCP server — the tool design comes from real usage, not theory.
Why this exists
When you use an AI agent to build a knowledge base, it can read and write pages. Without constraints:
- It overwrites pages you spent time curating.
- Files end up scattered across directories with no consistent structure.
- It updates
index.mdwithout recording what changed. - There's no audit trail of who changed what and why.
This server forces every write to go through a candidate review cycle. The agent proposes changes, you review and approve them, and only then does the wiki update.
What it does
- Candidate-first writes — formal pages, index updates, and public exports are proposals. Nothing gets written until you call
apply_candidate. - Immutable raw sources —
raw/files can only be created, never overwritten. Your source material stays intact. - Path safety — all operations stay within
wiki_root. Parent-traversal attempts are rejected at the path layer. - Structured linting —
run_lintreturns parsed results (errors, warnings, suggestions) as data, not a process exit code that breaks the MCP transport. - Change log — every mutation is logged with action, impact, and verification, with configurable retention.
- Search that tells you what to do next — formal pages and raw sources are ranked separately. Scope-aware search returns a
next_actionhint (read_pagevsread_raw_source). - Frontmatter validation — unknown fields, empty directory lists, and invalid retention values are caught before they reach your wiki.
Workflow

New / revised source added
↓
compile_page or create_update_candidate
↓
Review Candidate bundle (page, index, public-draft, log, source-manifest)
↓
apply_candidate after explicit approval
↓
run_lint
Agents return persisted Candidate bundles first. You (or your agent's human-in-the-loop) review the full change set before anything touches the formal wiki.
Quick Start
git clone https://github.com/jaronlu/llm-wiki-mcp.git
cd llm-wiki-mcp
uv sync --dev
cp config/examples.config.yaml config/config.yaml
uv run llm-wiki-mcp
Edit config/config.yaml for your machine. Keep it local and untracked.
wiki_root: ~/llm-wiki
allow_write_raw: false
allow_write_formal: false
allow_update_index: false
allow_modify_schema: false
log_retention_entries: 120
formal_dirs: [domains, entities]
raw_dirs: [raw]
workshop_dirs: [workshop]
non_formal_dirs: [drafts, reading]
workshop_dirs enables project packages whose root README.md is a formal
entity page and whose raw/ subtree contains project evidence:
llm-wiki/
├── workshop/
│ ├── agentic-rag-securities/
│ │ ├── README.md
│ │ └── raw/
│ └── wiki-mcp/
│ ├── README.md
│ └── raw/
├── domains/
├── entities/
└── raw/
MCP tools
| Tool | What it does |
|---|---|
init_wiki |
Creates or completes a wiki root with scaffolding |
inspect_wiki |
Checks if a directory is a valid wiki and reports status |
search_wiki |
Scope-aware search across formal pages and raw sources |
read_page |
Reads a formal page with parsed frontmatter and link analysis |
read_raw_source |
Reads raw source files (immutable view) |
create_raw_source |
Creates a new raw source (create-only, no overwrite) |
append_log |
Appends a structured change-log entry |
compile_page |
Builds a candidate formal page from raw sources |
create_update_candidate |
Builds a candidate index update |
apply_candidate |
Applies a previously-reviewed candidate bundle |
run_lint |
Runs structured lint checks and returns parsed results |
knowledge_health_review |
Reviews wiki health (coverage, orphans, stale pages) |
write_public_draft |
Creates a public-facing draft (candidate-first) |
validate_public_safety |
Checks that public exports don't leak sensitive content |
Mutation tools are conservative by default. Raw writes require allow_write_raw: true; applying candidates requires allow_write_formal: true.
Configuration
Config loading order:
- Built-in defaults.
- Project-local
config/config.yaml, when present.
The server intentionally ignores MCP host config path environment variables and root override environment variables, so the runtime source of truth stays in the repository-local config file.
Config validation rejects unknown top-level fields, nested directory names, empty directory lists, and non-positive log_retention_entries values.
Safety boundaries
- All paths must resolve under
wiki_root. init_wikicreates or completeswiki_rootby default when no explicitrootargument is provided.raw/writes are create-only and never overwrite existing files.- Formal page writes require
allow_write_formal: true;index.mdupdates, migrations, and public exports are candidate-first. .llm-wiki/source-manifest.jsontracks raw source digests without modifying page frontmatter.run_lintreturns structured lint data instead of treating lint failures as MCP transport failures.
MCP Host Config
[mcp_servers.llm_wiki]
command = "uv"
args = ["--directory", "/path/to/llm-wiki-mcp", "run", "llm-wiki-mcp"]
startup_timeout_sec = 120
The server always reads configuration from <repo>/config/config.yaml; no MCP host environment variable is needed.
Development
uv run ruff check .
uv run pytest
Contributing
See CONTRIBUTING.md for development setup and design rules.
License
MIT — see LICENSE.
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
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.