How to Use the Writer (AI Enterprise LLM) MCP in Pydantic AI

Build validated agents with Pydantic AI. Get reliable data structures from Writer (AI Enterprise LLM) for production code.

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Connect Writer (AI Enterprise LLM) MCP to Pydantic AI

Create your Vinkius account to connect Writer (AI Enterprise LLM) 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 Writer (AI Enterprise LLM) with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Structured Data Retrieval

The `ask_question` tool retrieves answers, but the real value comes when your agent validates that answer against a Pydantic schema. This ensures you get clean data structures from your Knowledge Graphs. If you need to build the knowledge base first, use `create_graph`. Then, upload files via `upload_file`, and let your agent run queries using `list_graphs` to ensure all necessary graphs are connected.

Controlled Content Generation

When generating content, you can mandate the output structure. Instead of just getting raw text from `text_completion`, your Pydantic agent expects a specific JSON format for structured data capture. For multi-step generation, use either `generate_application_content` or its async counterpart. Want to analyze something visual? The `analyze_vision` tool feeds results that you can then enforce into a predictable schema.

Reliable File Operations

File handling is critical. Your agent uses `get_file` and `list_files` to check metadata before doing anything else. If a file needs to be used for RAG, it's added via `add_file_to_graph`. The system also provides the `download_file` tool so your code can pull down the actual binary content reliably. If you need to remove data, use `remove_file_from_graph` rather than just deleting the file—it keeps the graph integrity intact.

Setup guide

Set up Writer (AI Enterprise LLM) 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": {
        "writer-ai-enterprise-llm-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Writer (AI Enterprise LLM) transactions")
print(result.output)

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Common questions about Writer (AI Enterprise LLM) MCP in Pydantic AI

The integration is about type safety. When your agent calls a tool like `chat_completion`, the response data isn't just arbitrary text; it must conform to the Pydantic models you defined, preventing silent errors.

Yes. By defining your schema upfront, your agent forces the model to structure its response. This guarantees that whether you're calling `translate_text` or running a query via `ask_question`, the data type is consistent.

Your agent catches this failure gracefully. The system provides tools like `list_application_jobs` and `retry_application_job`, allowing your Python code to handle the exception and retry the operation without crashing.

You manage it through controlled tool calls. Before any analysis, you check metadata using `get_file`. If the file is meant to be part of a graph, you must explicitly call `add_file_to_graph`.

This server handles file metadata, raw text content, structured knowledge graph relationships, and serialized application job results. All these are treated as sensitive inputs.

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