How to Use the Basecamp MCP in Pydantic AI

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

Install pydantic-ai-slimmcp and use the MCPToolset constructor with your Vinkius HTTP URL to load the Basecamp tools. Pass this toolset directly into the toolsets argument when defining your Pydantic AI Agent.

Connect Basecamp to your Pydantic AI agents with strict runtime validation for every project and task list.

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Connect Basecamp MCP to Pydantic AI

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Setup Basecamp with Pydantic AI

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Validate Basecamp to-dos at runtime with Pydantic AI.

The `list_todos` tool pulls task lists from Basecamp and validates the structure against your Pydantic AI schemas. If Basecamp returns a missing field or an unexpected format, your Pydantic AI system catches it instantly at the runtime boundary. This strict validation prevents your Pydantic AI agent from processing corrupt Basecamp task data or hallucinating deadlines. By combining `list_todolists` with Pydantic AI's type safety, you ensure your downstream workflows only run on clean, structured Basecamp data.

Read validated campfire logs using this MCP Server.

The `list_campfire_lines` tool extracts recent Basecamp chat messages and parses them into typed Pydantic AI models. Your Pydantic AI agent uses these models to verify that every Basecamp chat line contains valid author IDs and timestamps before running any analysis. If a Basecamp chat line matches your Pydantic AI schema, the agent processes it; if not, it throws a validation error instead of guessing. This keeps your automated Basecamp chat parsers highly reliable and predictable within the Pydantic AI framework.

Verify project metadata structures in Pydantic AI.

The `get_project` tool retrieves detailed Basecamp project metadata, which Pydantic AI validates against your defined configurations. Your Pydantic AI agent calls this tool alongside `list_projects` to confirm that every active Basecamp project matches your internal compliance standards. Because the Pydantic AI framework is model-agnostic, you can swap your underlying LLM while keeping your Basecamp validation schemas identical. Your Pydantic AI runtime checks on Basecamp data remain rock-solid whether you use OpenAI, Anthropic, or local models.

Setup guide

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

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

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

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Common questions about Basecamp MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and use the `MCPToolset` constructor with your Vinkius HTTP URL to load the Basecamp tools. Pass this toolset directly into the `toolsets` argument when defining your Pydantic AI Agent.

The Pydantic AI framework will fail loudly with a validation error rather than passing bad Basecamp data to your agent. This prevents the Pydantic AI agent from hallucinating based on incomplete or malformed Basecamp JSON payloads.

Yes, Pydantic AI is model-agnostic, so you can run local models to process Basecamp data fetched by `list_messages` and `list_todos`. The Pydantic AI schema validation runs locally in Python before the model ever sees the Basecamp text.

Yes, the Basecamp server supports both Streamable HTTP and SSE transports in Pydantic AI. You configure this connection directly when initializing your Pydantic AI `MCPToolset` with your Vinkius endpoint.

Your Basecamp API tokens used by Pydantic AI are managed securely within Vinkius's ephemeral, zero-trust V8 sandbox. This MCP Server handles raw credentials in an isolated environment, ensuring Pydantic AI only interacts with the validated schema output over HTTPS.

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