How to Use the Concord CLM MCP in Pydantic AI

Q: How does the agent retrieve available templates?

Your agent runs listtemplates to get a structured list of available contract layouts. The returned data is validated against Python types, allowing your agent to select the correct template ID safely.

Run type-safe Concord CLM MCP Server contract operations in Pydantic AI with strict runtime validation.

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Connect Concord CLM MCP to Pydantic AI

Create your Vinkius account to connect Concord CLM 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 Concord CLM with Pydantic AI

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Type-safe contract generation with Pydantic AI

Legal data cannot afford silent corruption. When your agent calls `create_agreement` using this MCP Server, Pydantic AI validates every single argument against strict Python types at runtime. If the model attempts to pass an invalid email or a malformed template ID, the framework throws a validation error immediately. This prevents bad data from ever reaching the Concord CLM API and ensures your contract records remain clean.

Strict schema validation for agreement search

Searching for contracts shouldn't rely on guesswork. Your agent can use `search_agreements_by_name` or `get_agreement` to locate files, with the framework validating the returned schema. If Concord CLM updates its API payload, your Pydantic AI agent will fail loudly instead of passing corrupted fields to your database. You get absolute certainty that the agreement metadata your system processes is structurally valid.

Validated signature dispatch workflows

Triggering `send_for_signature` requires precise input structures. This integration ensures your agent verifies the target email addresses and document IDs against strict Pydantic models before executing the signature request. You can combine this with `list_signed_agreements` to build reliable, type-safe automated billing or onboarding pipelines. Your code catches schema mismatches before they can cause operational errors.

Setup guide

Set up Concord CLM 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": {
        "concord-clm-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Concord CLM MCP in Pydantic AI

Use the MCPToolset class pointing to your Vinkius server URL and pass it to your Agent constructor. This unified approach automatically registers tools like `create_agreement` and `list_templates` with strict type validation.

The framework will raise a validation error instantly rather than letting your agent process corrupted data. This ensures operations like `get_agreement` or `list_users` always match your expected Python schemas.

Yes, Pydantic AI is model-agnostic. You can use OpenAI, Anthropic, Gemini, or local models to run tools like `send_for_signature` and `list_signed_agreements` with the same type-safe guarantees.

Your agent runs `list_templates` to get a structured list of available contract layouts. The returned data is validated against Python types, allowing your agent to select the correct template ID safely.

Your contract schemas, agreements, and user lists are processed locally in your application memory during validation. Vinkius operates a zero-trust, ephemeral MCP gateway that simply proxies the tool calls securely without storing any of your legal data.

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