How to Use the Insightly MCP in Pydantic AI

Q: Does this Insightly MCP Server support the latest Pydantic AI version?

Yes. It uses the MCPToolset pattern, providing a clean, unified interface for your agents to communicate with the CRM.

Use Pydantic AI with Insightly to ensure your CRM data is always validated and typed correctly.

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Connect Insightly MCP to Pydantic AI

Create your Vinkius account to connect Insightly 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 Insightly with Pydantic AI

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Claude

Perplexity

Type-safe CRM queries

Stop worrying about malformed data. Every response from `get_contact` or `list_tasks` is validated against your Pydantic models, failing immediately if the schema mismatches. This ensures your agent logic only runs on clean, verified data. It prevents silent errors when the CRM API returns unexpected fields or formats.

Validated list operations

Use `list_opportunities` and `list_leads` with full confidence. The MCP Server provides the data, and your Pydantic AI agent confirms it matches your expected structure. This workflow catches data issues before they reach your logic layer. It maintains high data integrity across your entire CRM-agent integration.

CRM resource discovery

Map your entire CRM ecosystem using `list_organisations` and `list_users`. Your agent uses these tools to build a local picture of your business relationships. Each tool call is protected by your runtime validation. If the CRM structure changes, your agent throws a validation error instead of processing bad data.

Setup guide

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

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

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

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Common questions about Insightly MCP in Pydantic AI

You define your Pydantic schemas to match the output of the MCP tools. The framework then validates every tool response against these models at runtime.

Yes. It uses the `MCPToolset` pattern, providing a clean, unified interface for your agents to communicate with the CRM.

The agent will raise a validation error immediately. This stops the process, preventing your system from acting on invalid or corrupted CRM information.

Yes. Since the framework is model-agnostic, you can use local models just as easily as cloud-based ones.

It operates in an ephemeral sandbox. The server only handles the transient transmission of your CRM records, ensuring your sensitive data is never persisted.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript