How to Use the Cliengo MCP in Pydantic AI

Q: How do I initialize the Cliengo server in Pydantic AI?

Initialize your MCP toolset using the unified MCPToolset pointing to your Vinkius HTTP URL. Pass this object directly into the toolsets argument of your Pydantic AI agent.

Q: Does Pydantic AI validate the output of listcliengoleads?

Yes, every lead record returned by listcliengoleads is run through Pydantic's validation engine. If a field mismatch occurs, the agent catches it instantly.

Q: Can Pydantic AI handle raw contact messages from Cliengo?

Your agent uses getcontactmessages to fetch raw text. The framework ensures the returned array conforms to your defined message schemas before passing it to the model.

Q: How do I check website configurations with Pydantic AI?

Run listcliengowebsites to get a structured list of domains. The agent validates the domain strings and installation statuses against your internal configuration models.

Q: How does this MCP Server secure my Cliengo user directory data?

When querying listcliengousers, all internal agent profiles are transmitted over TLS directly to your isolated V8 engine. Vinkius manages the credentials, so your Pydantic AI environment never exposes your raw API keys.

Validate Cliengo lead and chat data at runtime using Pydantic AI to prevent silent API errors.

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Connect Cliengo MCP to Pydantic AI

Create your Vinkius account to connect Cliengo 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 Cliengo with Pydantic AI

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Validate Cliengo lead payloads with Pydantic AI

`get_lead_details` fetches structured contact records from your chatbot database. This tool delivers raw JSON payloads containing phone numbers, emails, and custom metadata fields. Pydantic AI validates this incoming data against your strict Python models at runtime. If the Cliengo API returns an unexpected format, your system raises an explicit validation error instead of passing bad data down the line.

Type-check conversational logs using the MCP Server

`get_chat_history` retrieves the complete sequence of messages from any chatbot or WhatsApp conversation. This MCP tool parses raw text exchanges so your agent can extract specific user intent. Your Pydantic AI agent processes this history by enforcing type safety on every message object. This prevents your LLM from hallucinating fields or misinterpreting the conversation structure during analysis.

Monitor webhook configurations with Pydantic AI

`list_cliengo_webhooks` lists every configured endpoint receiving real-time lead updates from your account. This tool lets your agent inspect active webhooks to ensure they match your expected destination URLs. The Pydantic AI agent parses the webhook objects to confirm they conform to your internal schema. You catch broken endpoints or invalid configurations before they disrupt your CRM sync.

Setup guide

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

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

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

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Common questions about Cliengo MCP in Pydantic AI

Initialize your MCP toolset using the unified `MCPToolset` pointing to your Vinkius HTTP URL. Pass this object directly into the `toolsets` argument of your Pydantic AI agent.

Yes, every lead record returned by `list_cliengo_leads` is run through Pydantic's validation engine. If a field mismatch occurs, the agent catches it instantly.

Your agent uses `get_contact_messages` to fetch raw text. The framework ensures the returned array conforms to your defined message schemas before passing it to the model.

Run `list_cliengo_websites` to get a structured list of domains. The agent validates the domain strings and installation statuses against your internal configuration models.

When querying `list_cliengo_users`, all internal agent profiles are transmitted over TLS directly to your isolated V8 engine. Vinkius manages the credentials, so your Pydantic AI environment never exposes your raw API keys.

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