# CSV to JSON Converter with Schema Inference & Validation (`nibble/csv-json-schema-converter`) Actor Convert CSV files to clean, typed JSON. Auto-detects delimiter, infers a JSON Schema, and validates rows against your own schema. Ideal for APIs, data pipelines and AI agents. - **URL**: https://apify.com/nibble/csv-json-schema-converter.md - **Developed by:** [Simon Fletcher](https://apify.com/nibble) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 converted files This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### What does the CSV to JSON Converter do? **CSV to JSON Converter** turns messy CSV files into **clean, typed JSON** you can drop straight into an API, a database, or an AI agent. It **auto-detects the delimiter** (comma, semicolon, tab or pipe), **infers value types** (integer, number, boolean, null), builds a **JSON Schema** describing your data, and can **validate every row against a schema you provide**. Give it a URL, an uploaded file, or raw CSV text — it gives back structured records, not a wall of strings. Running on Apify means you get an HTTP API, scheduling, [integrations](https://apify.com/integrations) (Make, Zapier, n8n, Google Drive), run history, and access from the [Apify MCP server](https://mcp.apify.com/) so AI agents can call it directly. ### Why use the CSV to JSON Converter? - **Feed APIs and pipelines** — convert exports from spreadsheets, banks, CRMs and analytics tools into JSON your code can consume. - **Give AI agents clean data** — the output is compact structured JSON (no HTML, no nested junk), ideal for LLM tool use over the Apify MCP. - **Catch bad data early** — supply a target JSON Schema and get a per-row validation report instead of silent corruption. - **Stop hand-writing parsers** — delimiter sniffing, quoted fields, embedded newlines, ragged rows and encodings are handled for you. ### How to use the CSV to JSON Converter 1. Open the **Input** tab. 2. Provide your CSV one of three ways: paste **inline CSV text**, add **file URLs**, or **upload files** (delivered via key-value-store keys). 3. (Optional) Set a delimiter, toggle header/type inference, or paste a **target JSON Schema** to validate against. 4. Click **Start**. Each input file becomes one dataset item you can download as JSON, CSV, Excel or HTML. ### Input | Field | Type | Description | |-------|------|-------------| | `csvUrls` | array | Public URLs of CSV files to download and convert. | | `keyValueStoreKeys` | array | Keys of uploaded files in the run's key-value store. | | `csvText` | array | Raw CSV strings passed inline (great for API / agent callers). | | `delimiter` | string | Force a delimiter. Blank = auto-detect `, ; \t \|`. | | `hasHeader` | boolean | Treat the first row as column names (default true). | | `inferTypes` | boolean | Coerce to integer/number/boolean/null (default true). | | `trimWhitespace` | boolean | Strip whitespace from every cell (default true). | | `nullValues` | array | Values to treat as null (**replaces** the default set: empty, NA, N/A, null, nan, none). | | `maxRows` | integer | Cap data rows per file (0/blank = no cap). | | `targetSchema` | object | JSON Schema (Draft 2020-12) for a single row; enables per-row validation. | #### Example input ```json { "csvText": ["id,name,active,score\n1,Ada,true,9.5\n2,Grace,false,8.0"], "inferTypes": true } ```` ### Output Each input file produces **one dataset item**. You can download the dataset as JSON, CSV, Excel or HTML. ```json { "source": "csvText[0]", "status": "ok", "error": null, "rowCount": 2, "columnCount": 4, "columns": ["id", "name", "active", "score"], "records": [ { "id": 1, "name": "Ada", "active": true, "score": 9.5 }, { "id": 2, "name": "Grace", "active": false, "score": 8.0 } ], "inferredSchema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "array", "items": { "type": "object", "properties": { "id": { "type": "integer" }, "name": { "type": "string" }, "active": { "type": "boolean" }, "score": { "type": "number" } } } }, "validation": { "checked": false, "valid": true, "validRows": 2, "invalidRows": 0, "errors": [] }, "meta": { "delimiter": ",", "hasHeader": true, "typesInferred": true, "encoding": "utf-8", "emptyRowsSkipped": 0, "raggedRows": 0 } } ``` #### Output fields | Field | Description | |-------|-------------| | `source` | Where the file came from (URL, KVS key, or `csvText[i]`). | | `status` | `ok` when converted, `error` when unreadable/empty. | | `rowCount` / `columnCount` | Converted data-row and column counts. | | `columns` | Ordered column names (header names or `field_N`). | | `records` | The converted rows as typed JSON objects. | | `inferredSchema` | JSON Schema describing the records. | | `validation` | Per-row report vs your `targetSchema` (`checked=false` when none). | | `meta` | Delimiter, encoding, header flag, blank/ragged row counts. | ### Pricing / How much does it cost? This Actor is billed **pay-per-result**: one charge per file that converts into non-empty data. A file that is empty, unreadable, or has no data rows is returned with `status: "error"` and is **never charged**. See the Pricing tab for the current per-result rate. Converting a handful of files costs a fraction of a cent of platform compute; the value is in never writing another CSV parser. ### Tips & advanced options - **Leading zeros are preserved.** Values like `01234` or `00080` stay strings so zip codes, phone numbers and IDs are never corrupted into integers. Type inference is **per cell**, so a column with mixed values (e.g. `01234` and `90210`) can contain both strings and integers — set `inferTypes: false` to keep every value a string. - **Ragged rows are lossless.** Rows with more cells than the header get extra columns named `field_N` rather than dropping data. - **Validation.** Paste a `targetSchema` to get a `validation` block flagging exactly which rows and fields don't match — without failing the whole run. - **Big files.** Each file is returned as one dataset item, and Apify caps a single item at ~9 MB. A file whose converted JSON would exceed that is returned as a clear (unbilled) size-cap error — use `maxRows` to convert it in smaller batches, or sample the top of a large file cheaply. ### FAQ & support - **What formats can I export?** JSON, CSV, Excel and HTML, from the dataset. - **Does it store my data?** Only in your run's own dataset/key-value store, under your account. - **Known limitation:** type inference is per-cell (see Tips). Column-level type unification is a planned option. - Found a bug or want a feature? Use the **Issues** tab. # Actor input Schema ## `csvUrls` (type: `array`): Public URLs of CSV files to convert. Each is downloaded (with retries) and converted to structured JSON. Example: https://people.sc.fsu.edu/~jburkardt/data/csv/hw\_200.csv ## `keyValueStoreKeys` (type: `array`): Keys in this run's default key-value store that hold CSV bytes. This is how files uploaded via the Console form are passed to the Actor. ## `csvText` (type: `array`): Raw CSV strings passed inline — convenient for API or AI-agent callers that send the data directly instead of by URL. ## `delimiter` (type: `string`): Column delimiter. Leave blank to auto-detect comma, semicolon, tab or pipe. ## `hasHeader` (type: `boolean`): Treat the first row as column names. If off, columns are named field\_1, field\_2, … ## `inferTypes` (type: `boolean`): Coerce cells to integer / number / boolean / null. Leading-zero values (e.g. 007, 01234) are kept as strings to protect IDs and zip codes. Turn off to keep every value a string. ## `trimWhitespace` (type: `boolean`): Strip leading/trailing whitespace from every cell before conversion. ## `nullValues` (type: `array`): Cell values (case-insensitive) to treat as null. Defaults to empty, NA, N/A, null, nan, none. ## `maxRows` (type: `integer`): Cap the number of data rows converted per file (0 or blank = no cap). ## `targetSchema` (type: `object`): Optional JSON Schema (Draft 2020-12) for a single row/object. When set, every converted row is validated against it and a per-row validation report is returned. ## Actor input object example ```json { "csvText": [ "id,name,active,score\n1,Ada,true,9.5\n2,Grace,false,8.0" ], "hasHeader": true, "inferTypes": true, "trimWhitespace": 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 = { "csvText": [ "id,name,active,score\n1,Ada,true,9.5\n2,Grace,false,8.0" ] }; // Run the Actor and wait for it to finish const run = await client.actor("nibble/csv-json-schema-converter").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 = { "csvText": ["""id,name,active,score 1,Ada,true,9.5 2,Grace,false,8.0"""] } # Run the Actor and wait for it to finish run = client.actor("nibble/csv-json-schema-converter").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 '{ "csvText": [ "id,name,active,score\\n1,Ada,true,9.5\\n2,Grace,false,8.0" ] }' | apify call nibble/csv-json-schema-converter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nibble/csv-json-schema-converter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CSV to JSON Converter with Schema Inference & Validation", "description": "Convert CSV files to clean, typed JSON. Auto-detects delimiter, infers a JSON Schema, and validates rows against your own schema. Ideal for APIs, data pipelines and AI agents.", "version": "0.0", "x-build-id": "nk2ffYRu5hZ2ge6gm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nibble~csv-json-schema-converter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nibble-csv-json-schema-converter", "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/nibble~csv-json-schema-converter/runs": { "post": { "operationId": "runs-sync-nibble-csv-json-schema-converter", "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/nibble~csv-json-schema-converter/run-sync": { "post": { "operationId": "run-sync-nibble-csv-json-schema-converter", "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", "properties": { "csvUrls": { "title": "CSV URLs", "type": "array", "description": "Public URLs of CSV files to convert. Each is downloaded (with retries) and converted to structured JSON. Example: https://people.sc.fsu.edu/~jburkardt/data/csv/hw_200.csv", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "keyValueStoreKeys": { "title": "Key-value store keys (uploaded files)", "type": "array", "description": "Keys in this run's default key-value store that hold CSV bytes. This is how files uploaded via the Console form are passed to the Actor.", "items": { "type": "string" } }, "csvText": { "title": "Inline CSV text", "type": "array", "description": "Raw CSV strings passed inline — convenient for API or AI-agent callers that send the data directly instead of by URL.", "items": { "type": "string" } }, "delimiter": { "title": "Delimiter", "type": "string", "description": "Column delimiter. Leave blank to auto-detect comma, semicolon, tab or pipe." }, "hasHeader": { "title": "First row is a header", "type": "boolean", "description": "Treat the first row as column names. If off, columns are named field_1, field_2, …", "default": true }, "inferTypes": { "title": "Infer value types", "type": "boolean", "description": "Coerce cells to integer / number / boolean / null. Leading-zero values (e.g. 007, 01234) are kept as strings to protect IDs and zip codes. Turn off to keep every value a string.", "default": true }, "trimWhitespace": { "title": "Trim whitespace", "type": "boolean", "description": "Strip leading/trailing whitespace from every cell before conversion.", "default": true }, "nullValues": { "title": "Null tokens", "type": "array", "description": "Cell values (case-insensitive) to treat as null. Defaults to empty, NA, N/A, null, nan, none.", "items": { "type": "string" } }, "maxRows": { "title": "Max rows per file", "minimum": 0, "type": "integer", "description": "Cap the number of data rows converted per file (0 or blank = no cap)." }, "targetSchema": { "title": "Target JSON Schema (validation)", "type": "object", "description": "Optional JSON Schema (Draft 2020-12) for a single row/object. When set, every converted row is validated against it and a per-row validation report is returned." } } }, "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 } } } } } } } } } } ```