How to Use the Zealy MCP in Pydantic AI

Q: Can I use Pydantic AI for Zealy quest review processes?

Yes. After users submit claims, you run listreviews. The agent can then approve or reject these submissions using the reviewsubmission tool while maintaining strict data integrity.

Q: When deploying with Pydantic AI, how do I manage community content?

You can update existing goals with updatequest, and if you need to change a whole objective, run listquests first. This guarantees the parameters for any updates are correct.

Q: How does the Zealy MCP Server handle community secrets with Pydantic AI?

You can list all active secrets using listsecrets. Because of Pydantic's validation, you know exactly what data type is being read or written for these critical API tasks.

Guarantee correct Zealy updates by validating every action with Pydantic AI's runtime type safety.

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Connect Zealy MCP to Pydantic AI

Create your Vinkius account to connect Zealy 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 Zealy with Pydantic AI

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Perplexity

Manage user progress and rewards.

The `add_xp` tool lets you manually boost a user's XP count. If the API sends bad data, Pydantic AI fails loudly—you know it immediately. You can also use `remove_xp` for necessary rollbacks. For moderation, run `ban_user`. The agent ensures that any action taken against a member is structured correctly before execution.

Set up complex Zealy workflows with MCP Server.

Need to track community goals? Use `create_quest` to deploy new objectives. If you want automated data transfer, setting up webhooks is key; use `create_webhook`. The agent validates the webhook parameters before they're written. The system also lets you retrieve and manage secrets needed for API tasks using `list_secrets`.

Monitor community health and activity.

To get a clear picture of who's performing, use `get_leaderboard`. You can pull detailed user data via `get_user`, or find users by social handle with `search_users`. The agent also lets you list all modules in the community using `list_modules`, giving you a full scope of available content.

Setup guide

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

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

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

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Common questions about Zealy MCP in Pydantic AI

Pydantic AI validates every single MCP response against defined Python models. This means if the Zealy API returns unexpected data, your agent throws an error instead of silently corrupting your system.

The server touches basic user identification and social handles. The agent uses `get_user` and `search_users` to interact with these details, which are then validated by Pydantic.

Yes. After users submit claims, you run `list_reviews`. The agent can then approve or reject these submissions using the `review_submission` tool while maintaining strict data integrity.

You can update existing goals with `update_quest`, and if you need to change a whole objective, run `list_quests` first. This guarantees the parameters for any updates are correct.

You can list all active secrets using `list_secrets`. Because of Pydantic's validation, you know exactly what data type is being read or written for these critical API tasks.

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