Actor Testing
Pricing
Pay per usage
Actor Testing
Test your actors with varying inputs and expected outputs, duplicates, bad output fields, or unexpected log messages using Jasmine
0.0 (0)
Pricing
Pay per usage
10
Monthly users
1
Runs succeeded
85%
Last modified
a month ago
You can access the Actor Testing programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
1# Start Server-Sent Events (SSE) session and keep it running
2curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=pocesar/actor-testing"
3
4# Session id example output:
5# event: endpoint
6# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1faUsing Actor Testing via Model Context Protocol (MCP) server
MCP server lets you use Actor Testing within your AI workflows. Send API requests to trigger actions and receive real-time results. Take the received sessionId and use it to communicate with the MCP server. The message starts the Actor Testing Actor with the provided input.
1curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{
2 "jsonrpc": "2.0",
3 "id": 1,
4 "method": "tools/call",
5 "params": {
6 "arguments": {
7 "testSpec": "({ it, xit, moment, _, run, expect, expectAsync, input, describe }) => {\\n (input.resource ? [\\n '\''beta'\'',\\n ] : [\\n '\''latest'\'',\\n ]).forEach((build) => {\\n describe(`${build} version`, () => {\\n it('\''test something-task'\'', async () => {\\n const runResult = await run({\\n taskId: '\'''\'',\\n });\\n\\n await expectAsync(runResult).toHaveStatus('\''SUCCEEDED'\'');\\n await expectAsync(runResult).withLog((log) => {\\n expect(log)\\n .withContext(runResult.format('\''ReferenceError'\''))\\n .not.toContain('\''ReferenceError'\'');\\n expect(log)\\n .withContext(runResult.format('\''TypeError'\''))\\n .not.toContain('\''TypeError'\'');\\n });\\n\\n await expectAsync(runResult).withStatistics((stats) => {\\n expect(stats.requestsRetries)\\n .withContext(runResult.format('\''Request retries'\''))\\n .toBeLessThan(3);\\n\\n expect(stats.crawlerRuntimeMillis)\\n .withContext(runResult.format('\''Run time'\''))\\n .toBeWithinRange(0.1 * 60000, 10 * 60000);\\n });\\n\\n await expectAsync(runResult).withDataset(({ dataset, info }) => {\\n expect(info.cleanItemCount)\\n .withContext(runResult.format('\''Dataset cleanItemCount'\''))\\n .toBeGreaterThan(0);\\n\\n expect(dataset.items)\\n .withContext(runResult.format('\''Dataset items array'\''))\\n .toBeNonEmptyArray();\\n });\\n });\\n });\\n });\\n}",
8 "slackChannel": "#public-actors-tests-notifications",
9 "slackPrefix": "@lead-dev @actor-owner"
10},
11 "name": "pocesar/actor-testing"
12 }
13}'The response should be: Accepted. You should received response via SSE (JSON) as:
1event: message
2data: {
3 "result": {
4 "content": [
5 {
6 "type": "text",
7 "text": "ACTOR_RESPONSE"
8 }
9 ]
10 }
11}Configure local MCP Server via standard input/output for Actor Testing
You can connect to the MCP Server using clients like ClaudeDesktop and LibreChat or build your own. The server can run both locally and remotely, giving you full flexibility. Set up the server in the client configuration as follows:
1{
2 "mcpServers": {
3 "actors-mcp-server": {
4 "command": "npx",
5 "args": [
6 "-y",
7 "@apify/actors-mcp-server",
8 "--actors",
9 "pocesar/actor-testing"
10 ],
11 "env": {
12 "APIFY_TOKEN": "<YOUR_API_TOKEN>"
13 }
14 }
15 }
16}You can further access the MCP client through the Tester MCP Client, a chat user interface to interact with the server.
To get started, check out the documentation and example clients. If you are interested in learning more about our MCP server, check out our blog post.
Pricing
Pricing model
Pay per usageThis Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage.
