How to Use the 6sense MCP in Pydantic AI

Q: How do I initialize the 6sense MCP Server in Pydantic AI?

Install pydantic-ai-slimmcp and use the MCPToolset constructor with your Vinkius HTTP URL. Pass the toolset into your Agent instance to make tools like enrichcompany available.

Bring type-safe runtime validation to your 6sense lookups using Pydantic AI.

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Connect 6sense MCP to Pydantic AI

Create your Vinkius account to connect 6sense 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 6sense with Pydantic AI

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Validate 6sense firmographics at runtime with Pydantic AI

This MCP Server exposes `enrich_company` to your type-safe agents. When the agent pulls firmographic data, Pydantic AI validates the response against strict schemas. If the API returns unexpected field formats, your agent fails immediately instead of passing bad data downstream. It guarantees that your domain lookups always match your database schemas.

Type-safe visitor identification via MCP Server tools

Your agents can run `identify_company` to resolve visitor IPs. Pydantic AI parses the incoming JSON, ensuring fields like company name and industry are correctly typed strings. You do not have to worry about silent data corruption. The agent validates the output of the IP lookup before deciding whether to trigger a sales sequence or list the company in your CRM.

Parse marketing segments and people profiles safely

Use `list_segments` and `enrich_person` to gather intelligence on target leads. The framework ensures that email addresses and segment names are validated before your agent acts. This is crucial when using open-source LLMs that might otherwise hallucinate fields. By enforcing strict type validation on the server responses, your marketing pipelines stay clean.

Setup guide

Set up 6sense 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": {
        "6sense-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to 6sense tools.",
)

result = await agent.run("List recent 6sense transactions")
print(result.output)

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Common questions about 6sense MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and use the MCPToolset constructor with your Vinkius HTTP URL. Pass the toolset into your Agent instance to make tools like `enrich_company` available.

The framework will raise a validation error at runtime. This prevents your agent from processing broken payloads and ensures your pipeline only handles clean data.

Yes, the MCPToolset class supports both transport layers. You can connect to your Vinkius hosted MCP Server using either streamable HTTP or Server-Sent Events.

Yes, Pydantic AI is model-agnostic. You can run local models via Ollama or llama.cpp and still use the type-safe `list_segments` tool.

The framework runs locally or in your private cloud, while Vinkius handles the MCP Server in a zero-trust sandbox. Sensitive inputs like email addresses sent to `enrich_person` are processed ephemerally and never written to disk.

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