actors-mcp-server
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| APIFY_TOKEN | Yes | Your Apify API token | |
| ANTHROPIC_API_KEY | No | Your Anthropic API token, required only when running certain example clients |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tasks | {
"list": {},
"cancel": {},
"requests": {
"tools": {
"call": {}
}
}
} |
| tools | {} |
| logging | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search-actorsA | Search the Apify Store to FIND and DISCOVER what scraping tools/Actors exist for specific platforms or use cases. This tool provides INFORMATION about available Actors - it does NOT retrieve actual data or run any scraping tasks. Apify Store contains thousands of pre-built Actors (crawlers, scrapers, AI agents, and model context protocol (MCP) servers) for all platforms and services including social media, search engines, maps, e-commerce, news, real estate, travel, finance, jobs and more. Apify Store lists Actors published by Apify and by independent developers. Use this tool when user wants to:
This tool discovers Actors; it does not retrieve their output. IMPORTANT: When the user wants detailed data from a specific platform or is looking for scraping tools, prefer searching the Store first — a relevant Actor often already exists. When the user names a specific Actor, look up that Actor directly instead of searching. Usage:
Important limitations: This tool does not return full Actor documentation or detailed usage instructions - only summary information.
Each result lists the Actor's input fields with their types (e.g. Returns list of Actor cards with the following info:
|
| fetch-actor-detailsA | Get detailed information about an Actor by its ID or full name (format: "username/name", e.g., "apify/rag-web-browser"). Requires the exact ID or full name — do not construct a plausible-looking name and call this tool with it. If you only have a description, a partial name, or a name you have not seen in this conversation, find it with search-actors first. Use 'output' to control returned information:
The 'readme' field returns the summary when available, full README otherwise. Use when querying Actor details, documentation, or input requirements. It does not connect to an MCP server Actor; fetch-actor-mcp-tool-list lists its MCP tools. EXAMPLES:
|
| call-actorA | Call any Actor from the Apify Store. WORKFLOW:
If the actor name is not in "username/name" format, use search-actors to resolve the correct Actor first. MCP server Actors are not run here; call-actor-mcp-tool calls their MCP tools. IMPORTANT:
There are two ways to run Actors:
USAGE:
EXAMPLES:
|
| get-actor-runA | Get detailed information about a specific Actor run. Returns run result: status, storages (datasets/keyValueStores alias map), stats, summary, nextStep.
USAGE:
USAGE EXAMPLES:
|
| get-dataset-itemsA | Get items (rows) from a dataset — the output/results produced by an Actor run. Returns the rows themselves, not dataset metadata, counts, or a schema. When the user provides a datasetId and asks to retrieve results, output, data, or rows, call this tool directly. Default limit is 20. Use clean=true to skip empty items and hidden fields. USAGE:
USAGE EXAMPLES:
|
| get-key-value-store-recordA | Get the value stored under a specific key in a key-value store — a single record, not a listing of all keys. Requires the exact key name. The response preserves the original Content-Encoding; most clients handle decompression automatically. USAGE:
USAGE EXAMPLES:
|
| abort-actor-runA | Abort an Actor run that is currently starting or running. For runs with status SUCCEEDED, FAILED, ABORTING, ABORTED, or TIMED-OUT, this call has no effect. The results will include the updated run details after the abort request. USAGE:
USAGE EXAMPLES:
|
| fetch-actor-mcp-tool-listA | List an Actor's MCP tools with each tool's name, description, input schema, annotations, and metadata, as the server reports them. call-actor-mcp-tool calls one of them by name. Each call asks the server again; nothing is cached. It connects with your Apify API token. Connecting can start a Server Actor run in your account, kept until its idle timeout; a multi-tenant Server Actor charges per event. USAGE:
USAGE EXAMPLES:
|
| call-actor-mcp-toolA | Call one tool on an Actor's MCP server and return its result. fetch-actor-mcp-tool-list lists the Actor's MCP tools and their input schemas. It connects with your Apify API token. Connecting can start a Server Actor run in your account, kept until its idle timeout; a multi-tenant Server Actor charges per event. What the tool itself does depends on the Actor. Example call: {"actor": "username/name", "tool": "search", "arguments": {"query": "apify"}} USAGE:
USAGE EXAMPLES:
|
| search-apify-docsA | Search Apify and Crawlee documentation using full-text search. Do not also search the Apify Store unless the user asks to find Actors. You must explicitly select which documentation source to search using the docSource parameter: • docSource="apify" - Apify: Apify Platform documentation including: Platform features, SDKs (JS, Python), CLI, REST API, Academy (web scraping fundamentals), Actor development and deployment • docSource="crawlee-js" - Crawlee (JavaScript): Crawlee is a web scraping library for JavaScript. It handles blocking, crawling, proxies, and browsers for you. • docSource="crawlee-py" - Crawlee (Python): Crawlee is a web scraping library for Python. It handles blocking, crawling, proxies, and browsers for you. The results will include the URL of the documentation page (which may include an anchor), and a limited piece of content that matches the search query. Fetch the full content of the document using the fetch-apify-docs tool by providing the URL. When results contain both platform documentation ( |
| fetch-apify-docsA | Fetch the full content of an Apify or Crawlee documentation page by its URL. Use this after finding a relevant page with the search-apify-docs tool. USAGE:
USAGE EXAMPLES:
|
| report-problemA | Report a problem with Apify's MCP tools or Actors to the Apify team. Reports can describe missing tools or Actors, errors, timeouts, confusing results, steps that took several attempts, or tasks that the available tools could not complete. Reports help fix the problem for other users. Put what you were doing and what went wrong in "message". Do NOT include personal data, credentials, secrets, or verbatim private conversation content — describe the issue in your own words. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 12 tools
Most tools target clearly distinct resources/actions (search-actors vs fetch-actor-details, get-dataset-items vs get-key-value-store-record, search-apify-docs vs fetch-apify-docs). The main overlap is within the Actor introspection family (fetch-actor-details, fetch-actor-mcp-tool-list, call-actor-mcp-tool) and between search-actors and fetch-actor-details, where search results already include input fields — but descriptions clarify each purpose.
All names use kebab-case with a verb-first pattern (search-, fetch-, get-, call-, abort-, report-). The convention is fully predicable and readable across all 12 tools.
12 tools is well-scoped for a scraping platform covering discovery, execution, monitoring, storage retrieval, MCP, docs, and feedback. Each tool maps to a distinct capability in the workflow.
The core lifecycle (search → inspect → run → poll/abort → retrieve dataset/KV output) is fully covered, plus docs and MCP support. Minor gaps exist: no list-actor-runs, list-datasets, or log retrieval, so agents must rely on IDs returned from prior calls rather than enumerating resources.