How to Use the BunnyDoc MCP in Pydantic AI

Q: How do I remove a webhook subscription using BunnyDoc and Pydantic AI?

Use the unsubscribewebhook tool. Your agent can trigger this as part of your cleanup logic to stop receiving events for completed document cycles.

Use BunnyDoc with Pydantic AI for type-safe signature automation and document management.

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Connect BunnyDoc MCP to Pydantic AI

Create your Vinkius account to connect BunnyDoc 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 BunnyDoc with Pydantic AI

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Validate signature requests in Pydantic AI

Run `create_signature_request` with the confidence that Pydantic AI validates the response against your data models. If the server returns something unexpected, the agent stops immediately. You avoid silent failures when fetching data with `list_templates`. Every field returned by the MCP server is checked against your schema before your agent moves to the next step.

Verify document status using Pydantic AI

Use `get_envelope_status` to track signature progress. Your agent receives typed data, so you know exactly which fields are present for each signature request. It handles lists of envelopes via `list_envelopes` without risking hallucinated fields. You get a clean, validated object back every time you query your document status.

Audit team activity in Pydantic AI

Call `list_team_members` to retrieve your team roster in a type-safe format. Your agent processes the list and confirms who has access to your document templates. Manage your membership list with `add_team_member` knowing the response structure is enforced. The agent won't proceed unless the server confirms the action according to your model definitions.

Setup guide

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

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

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

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Common questions about BunnyDoc MCP in Pydantic AI

Install the slim MCP package and use the MCPToolset class to connect your endpoint. Pass this toolset into your Pydantic AI agent to enable tool execution.

Yes, that is the primary benefit. If the API response deviates from your Pydantic models, the agent raises a validation error rather than proceeding with corrupted or missing data.

Because the server handles the tool definitions and Pydantic AI handles the validation, you can swap between OpenAI, Anthropic, or local models without changing your toolset.

Use the `unsubscribe_webhook` tool. Your agent can trigger this as part of your cleanup logic to stop receiving events for completed document cycles.

We utilize runtime validation to ensure only expected schema types reach your agent. Your document identifiers and account statistics are handled through encrypted channels and never persisted in an insecure manner.

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