You can discover and run TinyRex Apify Store actors through MCP using your own APIFY_TOKEN, then retrieve capped dataset results.
List all 27 public TinyRex actors with name, title, description, Store URL, and example input; private/ToS-blocked actors are excluded.
Run an actor by short name or tinyrex/<name> with an input object and optional max_items (default 50), timeout_secs (default 180), and memory_mbytes.
Wait for the run to finish and return dataset items plus itemCount, runId, and run status.
Provides tools to run the TinyRex RSS feed scraper, enabling collection of items from RSS feeds via Apify.
Provides tools to run the TinyRex Shopify products scraper, enabling extraction of product data from Shopify stores via Apify.
Provides tools to run the TinyRex WooCommerce products scraper, enabling extraction of product data from WooCommerce stores via Apify.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TinyRex MCP look up the LEI for Siemens AG with gleif-lei-lookup"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Run TinyRex Apify Store actors from any MCP-capable AI agent (Claude Desktop, Cursor, and others) using your own APIFY_TOKEN.
This is a thin stdio Model Context Protocol server: it lists the public TinyRex actors and starts a run, then returns dataset items (capped for MCP hosts).
Actors are pay-per-event (PPE) on Apify. Failed or empty runs are often free under TinyRex pricing: check each actor’s Store page. You pay Apify with your own account; this MCP does not bill separately.
Python 3.11+:
pip install git+https://github.com/emirmrkaljevic/tinyrex-mcp.git
# or clone and:
# pip install -e .Requires an Apify API token in the environment as APIFY_TOKEN.
Related MCP server: veriloop
mcpServers){
"mcpServers": {
"tinyrex": {
"command": "tinyrex-mcp",
"env": {
"APIFY_TOKEN": "YOUR_APIFY_TOKEN"
}
}
}
}If the script is not on PATH, use the module form:
{
"mcpServers": {
"tinyrex": {
"command": "python",
"args": ["-m", "tinyrex_mcp"],
"env": {
"APIFY_TOKEN": "YOUR_APIFY_TOKEN"
}
}
}
}Tool | What it does |
| Lists all 27 public TinyRex actors (name, title, description, Store URL, example input). |
| Runs |
list_actors → find gleif-lei-lookup
run_actor with:
{
"actor": "gleif-lei-lookup",
"input": { "companyNames": ["Siemens AG"], "maxResultsPerName": 1 },
"max_items": 5
}Actor | Store |
tech-stack-detector | |
ats-jobs-scraper | |
shopify-products-scraper | |
workday-jobs-scraper | |
woocommerce-products-scraper | |
ted-tenders-scraper | |
eu-grants-scraper | |
remote-jobs-scraper | |
rss-feed-scraper | |
sitemap-scraper | |
contact-details-extractor | |
us-uk-tenders-scraper | |
tenderned-tenders-scraper | |
seo-on-page-audit | |
broken-link-checker | |
gleif-lei-lookup | |
uk-companies-house-lookup | |
bulk-website-screenshot | |
bulk-email-validator | |
pdf-ocr-text-extractor | |
npi-registry-lookup | |
lighthouse-audit | |
sec-edgar-filings-scraper | |
france-company-search | |
norway-company-search | |
finland-company-search | |
audio-video-transcriber |
Private / ToS-blocked actors are not exposed.
docker build -t tinyrex-mcp .
docker run --rm -i -e APIFY_TOKEN=YOUR_APIFY_TOKEN tinyrex-mcppython -m venv .venv && source .venv/bin/activate
pip install -e .
APIFY_TOKEN=... tinyrex-mcp # stdio MCPSmoke-test helpers (list tools + one cheap run) live in scripts/smoke_test.py.
MIT. Brand: TinyRex. Store username: tinyrex.
List all public TinyRex Apify Store actors this MCP can run.
Returns name, title, short description, Store URL, and an example input for each actor. Private / ToS-blocked actors are not included.
Inputs: no named parameters. Returns: result
Run a TinyRex Apify actor and return dataset items.
Args: actor: Actor name (e.g. "gleif-lei-lookup") or "tinyrex/". input: Actor input object (same fields as on the Apify Store). Use list_actors exampleInput as a starting point. max_items: Max dataset items to return (default 50). Total count is always included. timeout_secs: How long to wait for the run to finish (default 180). memory_mbytes: Optional memory override for the run (Apify default if omitted).
Inputs: actor (required), input, max_items, timeout_secs, +1 more. Returns: result
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 2 tools
The two tools have clearly distinct purposes: list_actors for discovery and run_actor for execution. There is no overlap, and the descriptions explain their relationship through the exampleInput field.
Both names follow a consistent snake_case verb_noun pattern: list_actors and run_actor. The verbs are distinct and the noun is consistent.
Only two tools are provided, which is below the typical 3-15 range and may feel thin for a server that might need run management. However, for the narrow scope of discovering and running actors, it could be sufficient.
The surface covers actor discovery and execution, including example inputs and result retrieval. However, it lacks operations for managing runs (e.g., status, cancellation, or retrieving past runs), which agents might need for long-running or async actors.
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