Install

npm install @mcp-rune/mcp-rune

New here? Start with the Quickstart — a real MCP server running in under ten minutes, no database or backend required.

Want to poke at a running example first? Scaffold the bookshelf example with one command — a 30-line model, a complete server:

npx @mcp-rune/create new my-app --template bookshelf

A taste

Write a model. That's the spec.

import { BaseModel } from '@mcp-rune/mcp-rune'

export class Book extends BaseModel {
  static override attributes = {
    title: { type: 'string', required: true },
    author: { type: 'string', required: true },
    status: { type: 'enum', values: ['reading', 'done'] },
    rating: { type: 'integer', validation: { min: 1, max: 5 } }
  }
}

From that one declaration, mcp-rune registers the full agent surface — and the tool list does not grow as you add models:

• Generic CRUD tools — list_models, find_records, create_model, update_model, delete_model cover every model you define.
• Prompts agents can follow — sections, validation, enum tables and turn-taking, derived from your attributes.
• Interactive MCP Apps — schema-driven create/edit/search UIs rendered inside Claude Desktop. No per-model HTML.
• OAuth 2.0, spec-compliant — discovery the way Inspector and strict clients expect (RFC 6749 / 8414 / 8707 / 9728).
• Docs, generated — every model and tool documents itself.

Both TypeScript and JavaScript are first-class — every example in the docs ships in both.

Retrieval that understands your domain

Most MCP servers can only hand the agent raw rows. mcp-rune indexes an entire dataset three ways — vectors, a relationship graph, and your domain vocabulary — then answers by meaning, aggregate, filter, or stratified sample. The agent reasons over findings, never the whole table.

• Recall by meaning — page summaries and stored findings are embedded with all-MiniLM-L6-v2 (384-dim) into local pgvector; the semantic mode ranks by cosine distance, no keywords.
• Walk the relationships — multi-hop ingest follows associations across models and stores the edges; the GraphRAG-aware summary strategies read that graph, not just flat fields.
• Grounded in your terms — a registry of concepts and business rules grounds every finding in your vocabulary.
• Act without re-exposing — analysis_act mutates a filtered subset server-side; matched IDs never return to the agent's context.

Six tools — analysis_ingest · analysis_query · analysis_store · analysis_act · analysis_summarize · analysis_clear — opt-in via ANALYSIS_ENABLED=true. Off by default — nothing loads, nothing runs, no startup cost until you turn it on. Local embeddings: no external vector DB, no embedding API.

Documentation

Visit the official documentation for the full guide library. Good places to start:

• Quickstart — your first server in ten minutes
• Project structure — where models, prompts, tools and apps live
• Prompt creation — the DSL that turns attributes into agent-fillable forms
• MCP Apps — interactive HTML UIs inside the client
• Analysis & GraphRAG — bring up pgvector and query thousands of records by meaning
• Data layer — the seam between the framework and any backend

Support

Having trouble? Ask in the official mcp-rune Discord — the community is happy to help you get unstuck.

For partnerships, security reports, or anything 1:1, email [email protected].

Contributing

New contributors welcome! Check out the Contributors Guide to get started, then browse the good first issues.

Join us on Discord to meet other contributors — we'll help you land your first contribution in no time.

License

MIT © the mcp-rune authors. No telemetry. Self-hosted by default.