How to Use the Kitetags MCP in Pydantic AI

Q: How does Pydantic AI ensure the data from Kitetags is correct?

When you add the Kitetags MCPToolset, Pydantic AI automatically fetches the server's OpenAPI schema. It then uses this schema to generate Pydantic models on the fly, validating every API response against them.

Q: How do I manage hashtag groups with Kitetags and Pydantic AI?

Your agent calls tools like creategroup and listgroups. The responses are validated Pydantic objects, so you can interact with them like any other Python object—accessing attributes like group.name with full type-safety.

Get type-safe, validated hashtag data for your Python agents. Pydantic AI and Kitetags ensure your social media automation never fails silently.

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Connect Kitetags MCP to Pydantic AI

Create your Vinkius account to connect Kitetags 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 Kitetags with Pydantic AI

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Get Analytics You Can Trust

This tool provides structured analytics for any hashtag. When your agent calls `get_tag_analytics`, the JSON response is automatically parsed and validated against a Pydantic model. You get a clean, typed object to work with. If the underlying API for Instagram or TikTok ever changes its response format, Pydantic AI will raise a `ValidationError` instantly. Your agent won't proceed with corrupted or unexpected data. It's correctness by default.

Build Correct-by-Construction Workflows

Use Kitetags to build reliable social media automation. The `create_group` and `create_tag` tools return structured objects that your code can depend on. There's no need to manually parse messy JSON strings. Imagine an agent that uses `search_tags`, then loops through the results to call `get_tag` for more detail. With Pydantic AI, you know every object in that loop conforms to your expected schema. This makes your agent's logic simpler and far more robust.

A Type-Safe MCP Server for Pydantic AI

The entire Kitetags toolset is available through the `MCPToolset`. Your agent can `list_groups`, `list_group_tags`, and manage your entire hashtag library with the guarantee that all inputs and outputs are validated. This approach prevents an entire class of bugs common in agent development. If your agent misinterprets a response or an API changes, you'll know immediately. It's the right way to build agents that handle real-world data.

Setup guide

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

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

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

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Common questions about Kitetags MCP in Pydantic AI

When you add the Kitetags `MCPToolset`, Pydantic AI automatically fetches the server's OpenAPI schema. It then uses this schema to generate Pydantic models on the fly, validating every API response against them.

Pydantic AI will raise a specific exception that you can catch and handle. It doesn't just pass along a string; it gives you structured error information, making your error-handling logic clean and predictable.

Yes. Pydantic AI is model-agnostic. You can connect the Kitetags `MCPToolset` to an agent that uses an OpenAI model, a local Llama model, or anything else Pydantic AI supports.

Your agent calls tools like `create_group` and `list_groups`. The responses are validated Pydantic objects, so you can interact with them like any other Python object—accessing attributes like `group.name` with full type-safety.

The server handles the hashtag and group names you provide and the analytics data it fetches. Your data's privacy is protected because each MCP server on Vinkius runs in a dedicated, sandboxed environment. All data in transit is encrypted, and your auth token secures access.

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