How to Use the Cohere (Embed & Rerank) MCP in Pydantic AI

Q: Is the MCP server compatible with Pydantic AI toolsets?

Yes, you use the MCPToolset to import the server. It handles the communication between your agent and the server using your preferred transport protocol.

Use Cohere (Embed & Rerank) with Pydantic AI for type-safe, validated document reranking and semantic searches.

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Connect Cohere (Embed & Rerank) MCP to Pydantic AI

Create your Vinkius account to connect Cohere (Embed & Rerank) 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 (Embed & Rerank) with Pydantic AI

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Validate Cohere outputs in Pydantic AI

Every response from `embed_texts` is checked against your Pydantic schemas at runtime. If the vector format is unexpected, the agent throws an error immediately. This prevents silent corruption. You know exactly what data your agent is working with, and the system fails fast if the API returns malformed data.

Type-safe reranking for Pydantic AI agents

Call `rerank_documents` and parse the output directly into a validated model. You gain the power of advanced reranking while keeping your agent's state strictly typed. There is no guessing what the reranker returns. Your code uses the validated list of chunks to make decisions with high confidence.

Control token limits in Pydantic AI

The `tokenize_text` tool returns the exact segmentation count. You use this to validate that your input fits within the constraints of your chosen model before you execute the logic. This ensures your agent never crashes due to context overflow. It enforces safety at the boundary of your application.

Setup guide

Set up Cohere (Embed & Rerank) 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-embed-rerank-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Cohere (Embed & Rerank) transactions")
print(result.output)

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Common questions about Cohere (Embed & Rerank) MCP in Pydantic AI

Yes, the framework validates every tool response. You define the structure, and the agent ensures that the data from the server matches it perfectly.

The agent will raise a validation error. This forces the system to stop rather than processing bad data, protecting the integrity of your agent's logic.

Yes, you use the `MCPToolset` to import the server. It handles the communication between your agent and the server using your preferred transport protocol.

The server is model-agnostic. Your Pydantic AI agent can use any supported LLM and still benefit from the same validated tool calls.

Data privacy is handled at the transport layer. Your sensitive text inputs undergo temporary processing, and the system clears all buffers after each validated response is returned.

Use it with your favorite AI tools

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