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Markdoc parser, component registry, and system prompt generator for LLM generative UI","maintainers":[{"name":"pnut","email":"hi@abhay.dev"}],"readme":"# @mdocui/core\n\nFramework-agnostic streaming parser, component registry, and system prompt generator for LLM generative UI. Parses Markdoc `{% %}` tag syntax from streamed LLM output into a typed AST that any renderer can consume.\n\nPart of the [mdocui](https://github.com/mdocui/mdocui) monorepo.\n\n## Install\n\n```bash\nnpm install @mdocui/core\n```\n\n## Overview\n\n`@mdocui/core` provides three things:\n\n1. **Streaming parser** -- incrementally tokenizes and parses Markdoc tags from chunked text (e.g. an LLM response stream).\n2. **Component registry** -- defines available components with Zod schemas so the parser can validate props and the prompt generator can describe them to the model.\n3. **Prompt generator** -- turns a registry into a system prompt section that teaches an LLM how to emit mdocUI markup.\n\n### How the parser separates prose from components\n\nThe LLM writes a single stream containing both standard markdown and `{% %}` component tags. The parser's job is to split that stream into two node types:\n\n- **Prose nodes** -- everything outside `{% %}` delimiters. This is standard markdown (headings, bold, lists, code blocks, etc.) and is passed through as-is for a markdown renderer to handle.\n- **Component nodes** -- everything inside `{% %}` delimiters. The parser extracts the tag name, parses attributes, and validates props against the registry's Zod schemas.\n\nThe `{%` sequence never appears in normal prose or fenced code blocks, so the tokenizer can reliably detect tag boundaries character-by-character during streaming -- no lookahead or backtracking required.\n\n---\n\n## API Reference\n\n### `Tokenizer`\n\nLow-level character-by-character tokenizer that splits raw text into `Token` objects. Handles `{% tag %}` boundaries, string quoting, and escape sequences. Most users should use `StreamingParser` instead.\n\n```ts\nimport { Tokenizer, TokenType } from '@mdocui/core'\n\nconst tokenizer = new Tokenizer()\n\nconst tokens = tokenizer.write('Hello {% button action=\"go\" label=\"Click\" /%}')\n// tokens[0] => { type: 'PROSE', raw: 'Hello ' }\n// tokens[1] => { type: 'TAG_SELF_CLOSE', raw: '{% button ... /%}', name: 'button', attrs: 'action=\"go\" label=\"Click\"' }\n\n// Flush any remaining buffer when the stream ends\nconst remaining = tokenizer.flush()\n\n// Reset for reuse\ntokenizer.reset()\n```\n\n**Token types:** `PROSE`, `TAG_OPEN`, `TAG_SELF_CLOSE`, `TAG_CLOSE`\n\n**Tokenizer states:** `IN_PROSE`, `IN_TAG`, `IN_STRING`\n\n---\n\n### `StreamingParser`\n\nIncremental parser that converts a stream of text chunks into an `ASTNode[]` tree. Tags are matched by name, nested correctly, and force-closed on flush if unclosed.\n\n```ts\nimport { StreamingParser } from '@mdocui/core'\n\nconst parser = new StreamingParser({\n  knownTags: new Set(['card', 'button']),\n  dropUnknown: true, // default -- silently drops unknown tags\n})\n\n// Feed chunks as they arrive from the LLM\nlet newNodes = parser.write('Here is a card:\\n{% card title=\"Hello\" %}')\nnewNodes = parser.write('Card body content')\nnewNodes = parser.write('{% /card %}')\n\n// Finalize -- force-closes any unclosed tags\nconst finalNodes = parser.flush()\n\n// Access the full AST\nconst allNodes = parser.getNodes() // ASTNode[]\n\n// Inspect errors and status\nconst meta = parser.getMeta() // ParseMeta\n```\n\n#### `ParserOptions`\n\n| Option | Type | Default | Description |\n|--------|------|---------|-------------|\n| `knownTags` | `Set<string>` | `new Set()` (allow all) | Tags the parser accepts. Empty set allows everything. |\n| `dropUnknown` | `boolean` | `true` | When `true`, unknown tags are silently dropped. When `false`, they are emitted as prose. |\n\n---\n\n### `ComponentRegistry`\n\nTyped store of component definitions. Used to generate the `knownTags` set for the parser and the system prompt for the LLM.\n\n```ts\nimport { ComponentRegistry, defineComponent } from '@mdocui/core'\nimport { z } from 'zod'\n\nconst registry = new ComponentRegistry({ coerce: true }) // LLM-friendly mode\n\nregistry.register(\n  defineComponent({\n    name: 'alert',\n    description: 'Displays a colored alert box',\n    props: z.object({\n      severity: z.enum(['info', 'warning', 'error']).describe('Alert severity'),\n      title: z.string().optional().describe('Optional heading'),\n    }),\n    children: 'any',   // 'none' | 'any' | string[]\n  })\n)\n\n// Batch register\nregistry.registerAll([alertDef, cardDef])\n\n// Query\nregistry.has('alert')       // true\nregistry.get('alert')       // ComponentDefinition | undefined\nregistry.names()            // ['alert', ...]\nregistry.all()              // ComponentDefinition[]\nregistry.knownTags()        // Set<string>\n\n// Validate props against the Zod schema\nconst result = registry.validate('alert', { severity: 'info' })\n// { valid: true, errors: [], props: { severity: 'info' } }\n```\n\n#### `RegistryOptions`\n\n| Option | Type | Default | Description |\n|--------|------|---------|-------------|\n| `coerce` | `boolean` | `false` | LLM-friendly mode: when validation fails, fall back to raw props instead of rejecting. Built-in definitions use `z.coerce.*` and case-insensitive enums, so most LLM output validates. The fallback is a safety net for edge cases. |\n\n---\n\n### `defineComponent`\n\nIdentity helper that returns a `ComponentDefinition` unchanged. Provides type inference when defining components outside a registry.\n\n```ts\nimport { defineComponent } from '@mdocui/core'\nimport { z } from 'zod'\n\nexport const myComponent = defineComponent({\n  name: 'my-component',\n  description: 'Does something useful',\n  props: z.object({\n    value: z.number().describe('A numeric value'),\n  }),\n  children: 'none',\n  streaming: { value: true }, // mark props that can stream partial values\n})\n```\n\n#### Always add `.describe()` to every prop\n\n`generatePrompt()` sources prop descriptions directly from Zod's `.describe()`. **Without it, the generated prompt shows a blank description and the LLM will guess — producing hallucinated field names, wrong types, and malformed values at runtime.**\n\nAlways describe:\n- **Array props** — type alone (`string[]`) tells the LLM nothing about element shape\n- **Enum props** — add context for when each variant should be used\n- **Any prop whose purpose isn't obvious from its name**\n\n```ts\n// ❌ LLM sees:  labels — string[]:\nlabels: z.array(z.string()),\n\n// ✓ LLM sees:  labels — string[]: Category labels for the X-axis, e.g. [\"Q1\",\"Q2\",\"Q3\"]\nlabels: z.array(z.string()).describe('Category labels for the X-axis, e.g. [\"Q1\",\"Q2\",\"Q3\"]'),\n```\n\nThe built-in definitions in `packages/core/src/definitions.ts` follow this convention throughout — use them as a reference.\n\n---\n\n### `generatePrompt`\n\nGenerates a complete system prompt from a registry. The prompt merges two layers:\n\n**Library layer** (auto-generated from the registry):\n- TAG SYNTAX reference — self-closing and body tag forms\n- COMPONENTS list — all registered components with prop types and allowed children\n- COMPOSITION rules — nesting examples showing card > grid > stat, form > inputs, etc.\n- STREAMING GUIDELINE — prose-before-components rendering advice\n\n**App layer** (your options):\n- `preamble` — domain context (\"You are an e-commerce assistant...\")\n- `groups` — organize components under headings with domain-specific notes\n- `additionalRules` — domain rules (\"Use stat inside grid for KPI dashboards\")\n- `examples` — domain examples showing expected output format\n\nThe final prompt structure:\n\n```\n[preamble]              ← your app context\n## TAG SYNTAX            ← auto from library\n## COMPONENTS            ← auto from registry + your groups\n## COMPOSITION           ← auto from library\n## STREAMING GUIDELINE   ← auto from library\n## RULES                 ← your additionalRules\n## EXAMPLE               ← your examples\n```\n\nYou never write syntax docs or component signatures — `generatePrompt()` handles that from the registry. You only provide domain context and usage guidance.\n\n```ts\nimport { generatePrompt, ComponentRegistry } from '@mdocui/core'\n\nconst registry = new ComponentRegistry()\n// ... register components ...\n\nconst prompt = generatePrompt(registry, {\n  preamble: 'You are a helpful assistant.',\n  additionalRules: [\n    'Always use a card for structured answers.',\n    'Never nest more than 3 levels deep.',\n  ],\n  examples: [\n    '{% card title=\"Weather\" %}\\nSunny, 72F\\n{% /card %}',\n  ],\n  groups: [\n    {\n      name: 'Layout',\n      components: ['stack', 'grid', 'card'],\n      notes: ['Use stack for vertical/horizontal layouts'],\n    },\n  ],\n})\n```\n\n#### `PromptOptions`\n\n| Option | Type | Description |\n|--------|------|-------------|\n| `preamble` | `string` | Text prepended before the syntax section |\n| `additionalRules` | `string[]` | Extra rules appended as a bullet list |\n| `examples` | `string[]` | Example markup blocks appended at the end |\n| `groups` | `ComponentGroup[]` | Groups components under named headings with optional notes |\n| `verbosity` | `'minimal' \\| 'default' \\| 'detailed'` | Prompt detail level. `minimal` outputs only component signatures (~90% fewer tokens). Default includes syntax docs, composition examples, and streaming guidelines |\n\n---\n\n### `parseAttributes`\n\nParses the attribute string inside a `{% tag ... %}` into a key-value record. Handles quoted strings (with escape sequences), arrays via JSON `[...]`, bare booleans, numbers, and null. Prototype pollution keys (`__proto__`, `constructor`, `prototype`) are silently skipped.\n\n```ts\nimport { parseAttributes } from '@mdocui/core'\n\nparseAttributes('action=\"go\" label=\"Click me\" count=42 disabled')\n// { action: 'go', label: 'Click me', count: 42, disabled: true }\n\nparseAttributes('options=[\"a\",\"b\",\"c\"] required=true')\n// { options: ['a', 'b', 'c'], required: true }\n```\n\n---\n\n### Prose parsing\n\nMarkdown parsing for prose nodes, with no rendering attached. Renderers walk the\nresult to build their own output, so nothing here emits markup and no renderer\nneeds `innerHTML` to display prose.\n\n```ts\nimport { parseBlocks, parseInline, sanitizeHref } from '@mdocui/core'\n\nparseBlocks('# Title\\n\\n- one\\n- two')\n// [{ type: 'heading', level: 1, content: 'Title' },\n//  { type: 'ulist', items: ['one', 'two'] }]\n\nparseInline('a **b** and [c](https://x.com)')\n// [{ type: 'text', content: 'a ' },\n//  { type: 'bold', content: 'b' },\n//  { type: 'text', content: ' and ' },\n//  { type: 'link', content: 'c', href: 'https://x.com' }]\n```\n\n`sanitizeHref` returns the href only if its scheme is safe to put in the DOM.\nRenderers must pass link hrefs through it — `javascript:` and `data:` URLs come\nback as `undefined`, meaning the link should render as plain text.\n\n```ts\nsanitizeHref('https://example.com') // 'https://example.com'\nsanitizeHref('javascript:alert(1)') // undefined\n```\n\nSupports bold, italic, bold+italic, strikethrough, inline code, links, headings\n(h1-h3), unordered and ordered lists, and paragraph breaks. `@mdocui/react`\nrenders this via `SimpleMarkdown`.\n\n---\n\n## Types\n\n### `ASTNode`\n\n```ts\ntype ASTNode = ProseNode | ComponentNode\n```\n\n### `ProseNode`\n\n```ts\ninterface ProseNode {\n  type: 'prose'\n  content: string\n}\n```\n\n### `ComponentNode`\n\n```ts\ninterface ComponentNode {\n  type: 'component'\n  name: string\n  props: Record<string, unknown>\n  children: ASTNode[]\n  selfClosing: boolean\n}\n```\n\n### `ComponentDefinition`\n\n```ts\ninterface ComponentDefinition {\n  name: string\n  description: string\n  props: z.ZodObject<z.ZodRawShape>\n  children?: 'none' | 'any' | string[]\n  streaming?: Record<string, boolean>\n}\n```\n\n### `ActionEvent`\n\nFired by interactive components in the renderer layer.\n\n```ts\ninterface ActionEvent {\n  type: 'button_click' | 'form_submit' | 'select_change' | 'link_click'\n  action: string\n  label?: string\n  formName?: string\n  formState?: Record<string, unknown>\n  tagName: string\n  params?: Record<string, unknown>\n}\n```\n\n### `ParseMeta`\n\nReturned by `parser.getMeta()`.\n\n```ts\ninterface ParseMeta {\n  errors: ParseError[]\n  nodeCount: number\n  isComplete: boolean\n  pendingTag?: string    // tag name being buffered (e.g. \"chart\")\n  bufferLength?: number  // bytes buffered for the pending tag\n}\n```\n\n### `ParseError`\n\n```ts\ninterface ParseError {\n  code: 'unknown_tag' | 'validation' | 'malformed' | 'unclosed'\n  tagName: string\n  message: string\n  raw?: string\n}\n```\n\n### `ValidationResult`\n\nReturned by `registry.validate()`.\n\n```ts\ninterface ValidationResult {\n  valid: boolean\n  errors: string[]\n  props?: Record<string, unknown>\n}\n```\n\n---\n\n## License\n\nSee the root [mdocui](https://github.com/mdocui/mdocui) repository for license details.\n","readmeFilename":""}