How to Use the Uptime.com MCP in Pydantic AI

Q: Can I reliably manage checks using Uptime.com and Pydantic AI?

Yes. The createcheck tool is type-safe; the agent must pass correctly structured inputs, preventing configuration errors before they hit the live system.

Q: What happens if I use Uptime.com and Pydantic AI to get stats?

When calling getcheckstats, your agent receives data guaranteed to match its schema, allowing for reliable calculations without worrying about missing or wrongly typed fields.

Validate Uptime.com service checks with Pydantic AI for guaranteed data correctness.

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Connect Uptime.com MCP to Pydantic AI

Create your Vinkius account to connect Uptime.com 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 Uptime.com with Pydantic AI

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MCP Server: Guaranteed Check Management

When the agent uses `create_check`, every parameter is validated against a schema, stopping bad data immediately. You can list all active checks with `list_checks` and know that the returned JSON structure will always be correct. If you need to modify monitoring rules, calling `update_check` ensures the payload matches required types before execution.

Structured Analysis of Service Health

The agent calls `get_check_stats`, and Pydantic validates that the returned statistics are properly formatted. You can also pull alert data using `list_recent_alerts`—and you'll never get corrupted fields. Getting root cause analysis via `get_root_cause_analysis` is safe because the output structure is guaranteed.

Controlled Access and Information Gathering

To see what services are monitored, use `list_monitoring_nodes`. The agent can also retrieve account details using `get_account_info`, knowing that the returned data fields match the expected model. This rigorous validation process applies to listing integrations (`list_integrations`) and checking specific service status via `get_check`.

Setup guide

Set up Uptime.com 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": {
        "uptimecom-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Uptime.com. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Uptime.com MCP in Pydantic AI

Pydantic validates the MCP Server's response against a defined Python model at runtime. This means if Uptime.com sends unexpected fields, your agent fails loudly instead of silently corrupting data.

Yes. The `create_check` tool is type-safe; the agent must pass correctly structured inputs, preventing configuration errors before they hit the live system.

When calling `get_check_stats`, your agent receives data guaranteed to match its schema, allowing for reliable calculations without worrying about missing or wrongly typed fields.

It's built for correctness. The validation layer ensures that regardless of how the monitoring data is served, your application receives predictable and reliable JSON payloads.

This server handles operational status data: check definitions, alert logs, monitoring node locations, account profile details, and service metrics.

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