How to Use the Freshworks MCP in Pydantic AI

Q: How do I set up the Freshworks MCP server with Pydantic AI?

First, pip install "pydantic-ai-slimmcp". Then, instantiate an MCPToolset with your Vinkius server URL and pass that toolset to your Pydantic AI Agent.

Q: Can I use Pydantic AI to combine Freshworks CRM contacts and helpdesk contacts?

Yes, and it's a great use case. Your agent can call listcrmcontacts and listhelpdeskcontacts, and Pydantic AI will return two lists of strongly-typed objects that you can then merge with confidence.

Build type-safe Freshworks agents with Pydantic AI. Get validated, structured data from your CRM and helpdesk, every time.

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Connect Freshworks MCP to Pydantic AI

Create your Vinkius account to connect Freshworks 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 Freshworks with Pydantic AI

Ask AI about this MCP

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Perplexity

Fail Loudly, Not Silently

Pydantic AI validates every response from the Freshworks server against a Pydantic model. If the API ever returns an unexpected value for a ticket from `get_ticket`, your agent will raise a `ValidationError` immediately. This prevents silent data corruption. You'll know right away if something is wrong with the data from `list_accounts` or `list_companies`, instead of letting your agent make bad decisions based on bad information.

Model-Agnostic Freshworks Agents

Pydantic AI isn't tied to one LLM provider. You can use the same Freshworks toolset with OpenAI, Anthropic, Gemini, or a local model you're running yourself. The integration code doesn't change. Your logic for calling `list_deals` or `list_agents` remains the same. You can swap the underlying LLM without rewriting any of your Freshworks integration code. That's the point of an open MCP standard.

Get Structured CRM & Support Data

When your agent calls `list_tickets`, you don't get back a blob of text or a messy dictionary. You get a list of Pydantic objects, with correct types for priority, status, and agent ID. This makes your agent's code much cleaner and more reliable. You can directly access `ticket.priority` instead of parsing strings, whether you're working with data from `list_crm_contacts` or `list_helpdesk_contacts`.

Setup guide

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

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

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

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Common questions about Freshworks MCP in Pydantic AI

First, `pip install "pydantic-ai-slim[mcp]"`. Then, instantiate an `MCPToolset` with your Vinkius server URL and pass that toolset to your Pydantic AI `Agent`.

Pydantic AI protects you from this. When your agent calls a tool like `get_ticket`, the framework will notice the missing field, fail validation, and raise a detailed error immediately.

Yes, and it's a great use case. Your agent can call `list_crm_contacts` and `list_helpdesk_contacts`, and Pydantic AI will return two lists of strongly-typed objects that you can then merge with confidence.

Yes, it supports both Streamable HTTP and Server-Sent Events (SSE). This allows your agent to process data from long-running calls, like fetching thousands of tickets from `list_tickets`, as it arrives.

Pydantic AI itself doesn't store your data. It validates the structure of information like contact lists and deal values as it passes from the secure Vinkius MCP server to your agent in memory. The connection is encrypted and the server environment is stateless.

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