How to Use the Lancerkit MCP in Pydantic AI

Q: How do I install the Lancerkit MCP Server tools for Pydantic AI?

Install the specific package variant using pip install "pydantic-ai-slimmcp". This includes the necessary dependencies for the unified toolset approach.

Q: How do I connect the Lancerkit MCP Server to my agent?

Create an instance of MCPToolset with your HTTP endpoint URL. Pass that object into the toolsets array when you build your agent.

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Connect Lancerkit MCP to Pydantic AI

Create your Vinkius account to connect Lancerkit 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 Lancerkit with Pydantic AI

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Perplexity

Type-Safe Lancerkit MCP Server

The `list_clients` tool forces every workspace account into a strict Pydantic model before your agent even sees the data from the MCP Server. If the Lancerkit API returns a missing field, the framework fails loudly and stops execution immediately. Once validated, the agent uses `get_invoice` to pull payments for a specific ID. You never have to worry about silent data corruption or hallucinated billing metrics because the runtime validation catches everything.

Validate Project Workloads

Executing `get_project` retrieves a single project's details and maps them to your predefined Python schemas. You just initialize `MCPToolset` with your Vinkius URL and pass it to your model-agnostic agent. The agent then runs `list_tasks` to check current working items. Since the framework supports Streamable HTTP, the connection stays reliable even when pulling heavy arrays of task data.

Parse Billable Metrics

Calling `list_services` fetches your configured billable items directly into the agent's context. You prioritize correctness, so the system verifies every pricing tier before doing any math. It pairs those services with `get_time_logs` to calculate accurate totals based on recorded hours. The agent also runs `get_status` to confirm the integration connection is healthy before finalizing any reports.

Setup guide

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

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

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

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Common questions about Lancerkit MCP in Pydantic AI

Install the specific package variant using `pip install "pydantic-ai-slim[mcp]"`. This includes the necessary dependencies for the unified toolset approach.

The framework throws a strict validation error at runtime. Your agent will not process the malformed response, preventing downstream failures in your application.

Create an instance of `MCPToolset` with your HTTP endpoint URL. Pass that object into the `toolsets` array when you build your agent.

The framework is entirely model-agnostic. You can run these tools against OpenAI, Anthropic, or local models while maintaining the exact same validation rules.

The integration reads recorded time logs and hours spent by your team. Vinkius isolates this execution within a secure sandbox environment, guaranteeing that your credentials and payloads remain completely separated from other instances.

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