How do I get an API Token for Freshchat?

Log in to Freshchat, navigate to Settings > API Tokens, and click on 'Generate Token' to retrieve your Bearer Token.

What is my 'Region Domain'?

Your domain depends on where your account is hosted (e.g. 'api.freshchat.com' for US, 'api.eu.freshchat.com' for Europe).

Can I send files or images through the agent?

Currently, the 'send_chat_message' tool focuses on sending text content. Supporting rich media typically requires separate upload operations.

Is it possible to see the chat history of a customer?

Yes! Use the 'list_chat_messages' tool and provide the Conversation ID. The agent will retrieve the historical message history for that thread.

Freshchat MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: Freshchat
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Freshchat through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Freshchat "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Freshchat?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Freshchat MCP Server

Connect your Freshchat account to any AI agent to automate your customer messaging and conversation management through the Model Context Protocol (MCP). Freshchat is a modern messaging software built for sales and support teams to engage with customers across web, mobile, and social channels. This MCP server enables you to track active chats, send real-time messages, and retrieve detailed user profiles directly through natural conversation.

Pydantic AI validates every Freshchat tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

Key Features

Conversation Oversight — List all active chats, fetch detailed conversation metadata, and monitor chat statuses (open, resolved) instantly.
Real-time Messaging — Post new messages to existing conversations to keep your support workflows moving fast.
User & Customer Data — Access detailed profile information for chat participants and search for users by email address.
Support Team Insights — List all support agents and team members to maintain full context of who is online and available.
Channel & Group Management — Access configured messaging channels and agent groups to understand your routing logic.
Message History — Retrieve the full message history for any specific conversation ID for audit and reporting.
Multi-Region Support — Seamlessly connect to your specific Freshchat data center (US, EU, IN, AU).

The Freshchat MCP Server exposes 12 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Freshchat to Pydantic AI via MCP

Follow these steps to integrate the Freshchat MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Freshchat with type-safe schemas

Why Use Pydantic AI with the Freshchat MCP Server

Pydantic AI provides unique advantages when paired with Freshchat through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Freshchat integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Freshchat connection logic from agent behavior for testable, maintainable code

Freshchat + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Freshchat MCP Server delivers measurable value.

Type-safe data pipelines: query Freshchat with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Freshchat tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Freshchat and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Freshchat responses and write comprehensive agent tests

Freshchat MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Freshchat to Pydantic AI via MCP:

check_account_status

Verify account configuration

get_agent_profile

Get agent metadata

get_chat_user_details

Get user metadata

get_conversation_details

Get chat metadata

list_agent_groups

List agent groups

list_chat_channels

List chat channels

list_chat_messages

List messages in a chat

list_chat_users

List chat participants

list_conversations

List active chats

list_support_agents

List support agents

search_chat_users

Find user by email

send_chat_message

Post a new message

Example Prompts for Freshchat in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Freshchat immediately.

"List all open conversations in my Freshchat account."

"Find the Freshchat user with the email 'customer@example.com'."

"Send a message to conversation 'conv_987': 'I am looking into this for you'."

Troubleshooting Freshchat MCP Server with Pydantic AI

Common issues when connecting Freshchat to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Freshchat + Pydantic AI FAQ

Common questions about integrating Freshchat MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Freshchat MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.