How to Use the Ayanza MCP in Pydantic AI

Q: How do I set up Ayanza with Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp". Use the MCPToolset class to connect to your Vinkius-hosted server. Pass this toolset into the Agent constructor to expose tools like createtask to your model.

Q: What is the modern way to load this toolset?

Use the unified MCPToolset class to connect to your MCP Server HTTP endpoint. Do not use the deprecated HTTP-specific classes. This unified approach handles both Streamable HTTP and SSE transports under the hood.

Build type-safe Python agents with Pydantic AI that validate every Ayanza task and wiki schema at runtime.

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Connect Ayanza MCP to Pydantic AI

Create your Vinkius account to connect Ayanza 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 Ayanza with Pydantic AI

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Type-safe Ayanza task validation with Pydantic AI

Validating every incoming payload from `get_task` against strict Python type models prevents silent data corruption in your project management pipelines. If the API structure changes unexpectedly, your system raises an explicit validation error. You initialize the toolset using the unified `MCPToolset` class. This connects your agent to the managed MCP Server. Your agent gains immediate, type-safe access to your workspace data.

Model-agnostic workspace querying via this MCP Server

This MCP Server ensures tools like `list_projects` work consistently across OpenAI, Anthropic, or local models. The framework handles the schema translation, so tools like `list_projects` work consistently across all LLMs. Running a multi-model setup lets you use cheaper models for basic task listing and smarter models for updating wikis. You swap the LLM backend with one line of code. The underlying tool schemas remain completely unchanged.

Safe user directory lookups

Your agent calls `list_users` to match task assignees with actual workspace members before making any updates. Pydantic AI ensures the returned emails and IDs match your expected data formats exactly. If the agent attempts to write an invalid user ID using `update_task`, the validation layer blocks the execution. This runtime check prevents bad database writes. You get a clean, predictable system that respects your database integrity.

Setup guide

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

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

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

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Common questions about Ayanza MCP in Pydantic AI

Install the package using `pip install "pydantic-ai-slim[mcp]"`. Use the `MCPToolset` class to connect to your Vinkius-hosted server. Pass this toolset into the `Agent` constructor to expose tools like `create_task` to your model.

Yes, the framework validates all incoming payloads from operations like `get_project` against runtime schemas. If the server returns unexpected fields, a validation error is triggered immediately. This protects your application from silent failures.

Yes, the framework is model-agnostic and supports local LLMs alongside major commercial APIs. You run `list_wiki_pages` or manage tasks using any model supported by the library. The tool definitions remain identical regardless of the backend.

Use the unified `MCPToolset` class to connect to your MCP Server HTTP endpoint. Do not use the deprecated HTTP-specific classes. This unified approach handles both Streamable HTTP and SSE transports under the hood.

Vinkius runs the server in a secure, zero-trust sandbox. Your workspace tokens, user lists, and task descriptions are processed in memory and never written to persistent logs. All connections use end-to-end TLS encryption.

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