# Agent Ready Data Cleaner (`topnetworks/agent-ready-data-cleaner`) Actor Clean and token-optimise HTML, JSON, scraped text, or URLs for LLM pipelines. Strip boilerplate, chunk by semantics, get token counts — feed your agents clean data, not nav bars. - **URL**: https://apify.com/topnetworks/agent-ready-data-cleaner.md - **Developed by:** [Les](https://apify.com/topnetworks) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / actor start This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## Agent-Ready Data Cleaner Apify Actor that transforms noisy URL/HTML/JSON/text inputs into clean, token-optimized chunks for LLM pipelines. ### Features - URL input fetch (10s timeout, redirects followed) then clean as HTML - HTML cleanup with configurable boilerplate removal - JSON flattening with null/empty removal - Text sanitization (control chars + blank-line dedupe) - Semantic/fixed/none chunking - Token counting via `gpt-tokenizer` (per chunk + total) - Metadata extraction + cleanliness scoring ### Input See `INPUT_SCHEMA.json`. ### Output One dataset item per input record with: - `chunks[]` with `tokenCount` and `chunkType` - `totalTokens` - size/compression stats - optional `metadata` (`title`, `description`, `canonicalUrl`, `wordCount`, `cleanlinessScore`) ### Pricing hint - $0.10 per start - $0.003 per result item ### Local test ```bash npm install node test-local.js ```` # Actor input Schema ## `inputs` (type: `array`): List of URL, HTML, JSON, or text inputs to clean. ## `outputFormat` (type: `string`): Format of the cleaned output: markdown, json, or plain text ## `chunkingStrategy` (type: `string`): How to split the cleaned content into chunks: semantic (by headings/paragraphs), fixed (by token count), or none ## `chunkSize` (type: `integer`): Target number of tokens per chunk when using semantic or fixed chunking. Default: 1000. ## `includeMetadata` (type: `boolean`): Include page metadata in output: title, description, canonical URL, word count, and cleanliness score ## `removeBoilerplate` (type: `boolean`): Strip navigation, footers, sidebars, ads, and other non-content elements from HTML ## Actor input object example ```json { "inputs": [ { "type": "url", "content": "https://example.com", "label": "example-url" } ], "outputFormat": "markdown", "chunkingStrategy": "semantic", "chunkSize": 1000, "includeMetadata": true, "removeBoilerplate": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "inputs": [ { "type": "url", "content": "https://example.com", "label": "example-url" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("topnetworks/agent-ready-data-cleaner").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "inputs": [{ "type": "url", "content": "https://example.com", "label": "example-url", }] } # Run the Actor and wait for it to finish run = client.actor("topnetworks/agent-ready-data-cleaner").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 '{ "inputs": [ { "type": "url", "content": "https://example.com", "label": "example-url" } ] }' | apify call topnetworks/agent-ready-data-cleaner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=topnetworks/agent-ready-data-cleaner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Agent Ready Data Cleaner", "description": "Clean and token-optimise HTML, JSON, scraped text, or URLs for LLM pipelines. Strip boilerplate, chunk by semantics, get token counts — feed your agents clean data, not nav bars.", "version": "0.1", "x-build-id": "u5dw389OLpKVUHy6J" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/topnetworks~agent-ready-data-cleaner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-topnetworks-agent-ready-data-cleaner", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/topnetworks~agent-ready-data-cleaner/runs": { "post": { "operationId": "runs-sync-topnetworks-agent-ready-data-cleaner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/topnetworks~agent-ready-data-cleaner/run-sync": { "post": { "operationId": "run-sync-topnetworks-agent-ready-data-cleaner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "inputs" ], "properties": { "inputs": { "title": "Input items", "type": "array", "description": "List of URL, HTML, JSON, or text inputs to clean.", "items": { "type": "object", "properties": { "type": { "title": "Type", "description": "Input type: url, html, json, or text", "type": "string", "enum": [ "url", "html", "json", "text" ] }, "content": { "title": "Content", "description": "The raw content or URL to clean", "type": "string" }, "label": { "title": "Label", "description": "Optional label for this input item", "type": "string" } }, "required": [ "type", "content" ] } }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "json", "text" ], "type": "string", "description": "Format of the cleaned output: markdown, json, or plain text", "default": "markdown" }, "chunkingStrategy": { "title": "Chunking strategy", "enum": [ "semantic", "fixed", "none" ], "type": "string", "description": "How to split the cleaned content into chunks: semantic (by headings/paragraphs), fixed (by token count), or none", "default": "semantic" }, "chunkSize": { "title": "Target chunk size (tokens)", "minimum": 100, "maximum": 8000, "type": "integer", "description": "Target number of tokens per chunk when using semantic or fixed chunking. Default: 1000.", "default": 1000 }, "includeMetadata": { "title": "Include metadata", "type": "boolean", "description": "Include page metadata in output: title, description, canonical URL, word count, and cleanliness score", "default": true }, "removeBoilerplate": { "title": "Remove boilerplate", "type": "boolean", "description": "Strip navigation, footers, sidebars, ads, and other non-content elements from HTML", "default": true } } }, "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 } } } } } } } } } } ```