# Document Structure Extractor — Markdown to JSON outline (`shoebill-dev27/doc-structure-extractor`) Actor Turn Markdown documents into structured JSON: nested heading tree with section text, fenced code blocks, links, parsed tables, and size statistics. Pure parsing, no LLM cost. - **URL**: https://apify.com/shoebill-dev27/doc-structure-extractor.md - **Developed by:** [Shinobu Otani](https://apify.com/shoebill-dev27) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Document Structure Extractor Turn Markdown documents into structured JSON — heading tree, section text, code blocks, links, and parsed tables. Pure parsing, deterministic, no LLM cost. ### What it does For each input document it extracts: - **Title** (first `#` heading) and preamble text - **Nested section tree**: level, heading, body text, character counts, children — fenced code blocks never miscounted as headings - **Code blocks** with language tags and line numbers - **Links** (`[text](url)`) - **Tables** parsed into header + rows - **Stats**: lines, characters, heading and code-block counts ### Input ```json { "documents": ["# Guide\n\nIntro.\n\n## Setup\n\n```bash\npip install x\n```"] } ```` ### Output (one dataset item per document) ```json { "title": "Guide", "sections": [ { "level": 1, "heading": "Guide", "text": "Intro.", "children": [{"level": 2, "heading": "Setup", "...": "..."}] } ], "code_blocks": [{"lang": "bash", "code": "pip install x", "line": 7}], "links": [], "tables": [], "stats": {"lines": 9, "chars": 52, "headings": 2, "code_blocks": 1} } ``` ### Typical uses - Building tables of contents / outlines for documentation sites - Feeding section-level structure into RAG ingestion pipelines - Auditing docs: section sizes, code-block coverage, dead-link candidates # Actor input Schema ## `documents` (type: `array`): Markdown documents to parse; one dataset item is produced per document ## Actor input object example ````json { "documents": [ "# Sample Document\n\nIntro paragraph with a [link](https://example.com).\n\n## Section One\n\nText under section one.\n\n| col a | col b |\n| --- | --- |\n| 1 | 2 |\n\n```python\nprint('hello')\n```" ] } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ````javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "documents": [ "# Sample Document\n\nIntro paragraph with a [link](https://example.com).\n\n## Section One\n\nText under section one.\n\n| col a | col b |\n| --- | --- |\n| 1 | 2 |\n\n```python\nprint('hello')\n```" ] }; // Run the Actor and wait for it to finish const run = await client.actor("shoebill-dev27/doc-structure-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ```` ## Python example ````python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "documents": ["""# Sample Document Intro paragraph with a [link](https://example.com). ## Section One Text under section one. | col a | col b | | --- | --- | | 1 | 2 | ```python print('hello') ```"""] } # Run the Actor and wait for it to finish run = client.actor("shoebill-dev27/doc-structure-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ```` ## CLI example ````bash echo '{ "documents": [ "# Sample Document\\n\\nIntro paragraph with a [link](https://example.com).\\n\\n## Section One\\n\\nText under section one.\\n\\n| col a | col b |\\n| --- | --- |\\n| 1 | 2 |\\n\\n```python\\nprint('\''hello'\'')\\n```" ] }' | apify call shoebill-dev27/doc-structure-extractor --silent --output-dataset ```` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shoebill-dev27/doc-structure-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Document Structure Extractor — Markdown to JSON outline", "description": "Turn Markdown documents into structured JSON: nested heading tree with section text, fenced code blocks, links, parsed tables, and size statistics. Pure parsing, no LLM cost.", "version": "0.1", "x-build-id": "ux1xBCWZO2gVtVrfQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shoebill-dev27~doc-structure-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shoebill-dev27-doc-structure-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shoebill-dev27~doc-structure-extractor/runs": { "post": { "operationId": "runs-sync-shoebill-dev27-doc-structure-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shoebill-dev27~doc-structure-extractor/run-sync": { "post": { "operationId": "run-sync-shoebill-dev27-doc-structure-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "documents" ], "properties": { "documents": { "title": "Documents", "type": "array", "description": "Markdown documents to parse; one dataset item is produced per document", "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```