How to Use the Grepsr MCP in Pydantic AI

Q: Does the Pydantic AI integration support webhooks for Grepsr scraper runs?

Yes, it does. Your agent can use createwebhook to register URLs that receive payload alerts when a crawl completes. You can verify active webhooks using listwebhooks within your typed Python code.

Run type-safe web scraping pipelines with Pydantic AI and get validated data payloads.

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Connect Grepsr MCP to Pydantic AI

Create your Vinkius account to connect Grepsr to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Grepsr with Pydantic AI

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Type-Safe Web Data Extraction

The `get_latest_data` tool retrieves your scraped records over an MCP connection and feeds them directly into Pydantic AI's validation engine. If the scraper payload structure changes unexpectedly, your Python code throws a validation error immediately. This prevents silent data corruption in your production databases. By enforcing strict schemas on the data returned by `get_report_data`, you ensure that only clean, well-formed records enter your downstream applications.

Programmatic Control Over Scraper Execution

The `run_report` tool gives your type-safe agents the power to kick off fresh crawl jobs programmatically. Before starting, the agent can check `get_usage_stats` to verify that your account has enough remaining API credits. If the run is already in progress, the agent calls `get_report_history` to track the execution status. This ensures your code handles pending, running, or failed scraper states without crashing or hanging indefinitely.

Manage Scraper Projects and Webhooks via MCP Server

The `list_projects` tool lets your agent inspect your entire active scraping catalog with complete type safety. Your agent can verify which reports belong to which project using `list_reports` before initiating any data requests. You can also manage your data routing by calling `list_webhooks` to ensure your notifications point to the correct endpoints. Every tool response is validated against Pydantic models, guaranteeing structural integrity across your entire automation pipeline.

Setup guide

Set up Grepsr MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "grepsr-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Grepsr tools.",
)

result = await agent.run("List recent Grepsr transactions")
print(result.output)

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Common questions about Grepsr MCP in Pydantic AI

Use the MCP configuration with your Vinkius HTTP endpoint URL and pass it to your Agent's toolsets parameter. This unified approach handles connection management and tool discovery automatically.

The agent will fail loudly with a validation error at runtime. This prevents corrupted web scraping data from silently polluting your application databases or downstream analytics pipelines.

Yes, absolutely. Your agent can parse user requests, find the correct report using `list_reports`, and trigger the crawl with `run_report`. The entire workflow remains strictly typed and validated.

Yes, it does. Your agent can use `create_webhook` to register URLs that receive payload alerts when a crawl completes. You can verify active webhooks using `list_webhooks` within your typed Python code.

Your account credentials and raw scraped datasets are completely isolated. Vinkius manages your API credentials inside a zero-trust, ephemeral V8 sandbox, keeping your sensitive scraping keys isolated from the LLM.

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