MCP Proof: Bounded MCP Evaluation
Under maintenancePricing
Pay per usage
Run a bounded known-answer evaluation set against an MCP server, snapshot tool schemas, grade deterministically, and return an evidence receipt. Not certification.
You can access the MCP Proof: Bounded MCP Evaluation 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 API & Integrations in Apify Console.
1from apify_client import ApifyClient2
3# Initialize the ApifyClient with your Apify API token4# Replace '<YOUR_API_TOKEN>' with your token.5client = ApifyClient("<YOUR_API_TOKEN>")6
7# Prepare the Actor input8run_input = {9 "url": "https://mcp.example.com/mcp",10 "evaluationSet": {11 "schema": "mcp-proof/evaluation-set/1",12 "name": "my-smoke-set",13 "version": "1.0.0",14 "expectedTools": ["lookup_zip"],15 "cases": [{16 "id": "zip-97201-portland",17 "tool": "lookup_zip",18 "arguments": { "zip": "97201" },19 "expect": {20 "kind": "textContainsAll",21 "values": ["Portland"],22 },23 }],24 },25}26
27# Run the Actor and wait for it to finish28run = client.actor("savagnic/mcp-proof").call(run_input=run_input)29
30# Fetch and print Actor results from the run's dataset (if there are any)31print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")32for item in client.dataset(run.default_dataset_id).iterate_items():33 print(item)34
35# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-startThe Apify API client for Python is the official library that allows you to use MCP Proof: Bounded MCP Evaluation API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
$pip install apify-clientOther API clients include: