How to Use the Tactile CRM MCP in Pydantic AI

Q: How do I trust opportunity metrics with Tactile CRM and Pydantic AI?

The agent uses getopportunitydetails, and because of the Pydantic validation layer, you know that every field returned—like deal value or stage—is correct. This eliminates data uncertainty.

Q: Does Tactile CRM and Pydantic AI handle contact details securely?

Yes. You use getcontactdetails to pull specific person records, guaranteeing the phone numbers and emails match your defined Python types. This structured approach keeps data reliable.

Q: What is the best way to review activity history with Tactile CRM and Pydantic AI?

You use listactivities to gather all tasks, calls, and emails. The validation layer ensures that even complex logs are returned in a predictable and usable structure.

Q: Can I check company details with Tactile CRM and Pydantic AI?

Absolutely. You call getcompanydetails to retrieve specific organization data, knowing the output will pass all type checks defined in your Pydantic models.

Ensure data correctness with Pydantic AI. Type-safe CRM actions from Tactile CRM.

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Setup Tactile CRM with Pydantic AI

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Validate Company Records

Need to know if a company record is clean? The agent calls `get_company_details` and validates the output against defined Pydantic models. If the API sends bad data, your agent fails loud—no silent corruption. You can verify all existing accounts by calling `list_companies`, ensuring that every returned object adheres to your strict type definitions.

Verify Deal Status and History

The system uses `get_opportunity_details` to pull deal data. Because of Pydantic validation, you know exactly what fields you're working with—no hallucinated metrics. Check the entire funnel using `list_opportunities`. This guarantees that every stage and metric returned is correctly typed for your downstream logic.

Structure Contact Lookups

The agent uses `get_contact_details` to pull person information. Since it's type-safe, you can trust the phone numbers and emails without worrying about unexpected data formats. Listing all contacts via `list_contacts` ensures that even when querying massive lists, every record conforms to your expected schema.

Setup guide

Set up Tactile CRM 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": {
        "tactile-crm-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Tactile CRM MCP in Pydantic AI

The agent uses `get_opportunity_details`, and because of the Pydantic validation layer, you know that every field returned—like deal value or stage—is correct. This eliminates data uncertainty.

Yes. You use `get_contact_details` to pull specific person records, guaranteeing the phone numbers and emails match your defined Python types. This structured approach keeps data reliable.

You use `list_activities` to gather all tasks, calls, and emails. The validation layer ensures that even complex logs are returned in a predictable and usable structure.

Absolutely. You call `get_company_details` to retrieve specific organization data, knowing the output will pass all type checks defined in your Pydantic models.

It manages company details, contact records, and sales opportunities. Critically, every single piece of returned data is validated against a schema before the agent uses it.

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