How to Use the Coassemble MCP in Pydantic AI

Q: How do I configure Coassemble with Pydantic AI?

Install the MCP-enabled Pydantic AI library and use MCPToolset with your Vinkius HTTP endpoint. Pass the toolset directly to your agent's constructor.

Q: Can I use Pydantic AI to list all Coassemble members?

Yes. Your agent can call listcoassemblemembers to get a structured list of users. The MCP framework guarantees the returned list matches your expected schema.

Run type-safe Coassemble workflows with Pydantic AI to validate student enrollments and training data at runtime.

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Connect Coassemble MCP to Pydantic AI

Create your Vinkius account to connect Coassemble 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 Coassemble with Pydantic AI

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Validate LMS Payloads with Pydantic AI

The `get_training_completion_results` tool stops you from worrying about malformed API responses breaking your production training pipelines. By connecting this MCP Server, Pydantic AI enforces strict schema validation on every piece of data returned from your LMS. When your agent calls the tool, the framework checks the response against Python type hints. If the API returns a string where an integer was expected, the system raises an error immediately instead of passing bad data down the line.

Type-Safe Student Enrollment and Grouping

Executing `get_member_group_associations` ensures your user directories match up exactly when managing cohorts. Your agent uses this MCP tool to verify a student's current status before moving them. The agent can safely execute `enroll_member_in_course` because the input arguments are validated against strict Pydantic models before the network request is even sent.

Inspect Course Details Without Silent Failures

Querying active classes with `list_coassemble_courses` lets you build reliable educational dashboards. Your agent queries active classes and parses the output into strongly typed Python objects. If you need to drill down into a specific class, the agent calls `get_course_training_details` knowing that the resulting dataset is fully structured and safe to use in downstream logic.

Setup guide

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

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

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

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Common questions about Coassemble MCP in Pydantic AI

Install the MCP-enabled Pydantic AI library and use `MCPToolset` with your Vinkius HTTP endpoint. Pass the toolset directly to your agent's constructor.

The framework will throw a validation error immediately. This prevents your agent from hallucinating properties or processing corrupted student records.

Yes. Your agent can call `list_coassemble_members` to get a structured list of users. The MCP framework guarantees the returned list matches your expected schema.

No. Vinkius hosts the server for you. Your agent connects to the remote HTTP endpoint, so you don't have to manage any local Node or Python runtimes.

All transactions are isolated in secure, single-tenant sandboxes. Your private keys and member completion scores are never exposed to the LLM provider, as the Vinkius gateway handles authorization out-of-band.

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