How to Use the Cohere (AI Platform) MCP in Pydantic AI

Ensure type safety for your agents with Cohere (AI Platform) and Pydantic AI.

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Connect Cohere (AI Platform) MCP to Pydantic AI

Create your Vinkius account to connect Cohere (AI Platform) 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 Cohere (AI Platform) with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Validate outputs with Pydantic AI models

Every response from `generate_text` or `chat_generation` is checked against your defined Pydantic schemas. If the API returns garbage, the agent raises a validation error instead of crashing silently. This keeps your data pipeline predictable. You know exactly what fields you're getting back before your downstream code tries to read them.

Use Cohere (AI Platform) for precise classification

Run `classify_inputs` to force model outputs into your predefined categories. Since this is integrated with Pydantic AI, you get strict type checking on every classification label. `tokenize_text` returns the exact segmentation data you need for your logic. You can build runtime checks to ensure your input strings don't exceed your model's capacity.

Organize your data with Cohere (AI Platform) tools

Call `rerank_documents` to get a structured array that complies with your type definitions. This makes handling search results in your agent code straightforward and reliable. `generate_embeddings` provides the dense vectors you need for your math-heavy agent logic. You can define a Pydantic model for these vectors to keep your code clean and type-safe.

Setup guide

Set up Cohere (AI Platform) 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": {
        "cohere-ai-platform-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Cohere (AI Platform) transactions")
print(result.output)

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Common questions about Cohere (AI Platform) MCP in Pydantic AI

Pydantic AI validates the tool output against your models at runtime. If the server returns a result that doesn't match your schema, the agent will throw a validation error immediately.

Yes, you define a class that inherits from BaseModel and the agent will automatically attempt to coerce the MCP tool output into that structure. It's the best way to avoid hallucinated fields.

You can use the SSE transport with the MCPToolset to handle streaming responses. This keeps your agent responsive while waiting for the API to finish processing.

Pydantic AI will catch the mismatch during the deserialization step. You'll get a clear error message instead of the agent trying to operate on faulty data.

Your agent connects to the Vinkius server over an encrypted channel. The server acts as a stateless bridge, meaning your private data is never cached or stored on our side after the Pydantic model validation is complete.

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