How to Use the MonkeyLearn MCP in Pydantic AI

Q: How do I register the MonkeyLearn MCP server inside a Pydantic AI agent?

Install the library via pip install "pydantic-ai-slimmcp" and instantiate MCPToolset with your Vinkius HTTP endpoint. Pass this toolset into the toolsets argument of your Agent constructor.

Q: Does Pydantic AI validate the raw responses returned by MonkeyLearn?

Yes, every payload returned by tools like classifytext is parsed and validated against internal Pydantic models. This ensures your agent only acts on structurally sound NLP data.

Q: Can I use custom classification models with MonkeyLearn in Pydantic AI?

Yes, you can specify your custom model ID when calling classifytext. The tool fetches the corresponding tags, which Pydantic AI then parses into your typed structures.

Q: How can my agent discover what extractors are available?

Your agent can run listextractors to retrieve a list of all active extraction models. This allows the system to choose the correct model dynamically based on the input text.

Validate every text classification and extraction schema at runtime using Pydantic AI.

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Connect MonkeyLearn MCP to Pydantic AI

Create your Vinkius account to connect MonkeyLearn 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 MonkeyLearn with Pydantic AI

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Classify text with strict Pydantic AI validation.

The `classify_text` tool returns structured tags that are validated against your Pydantic schemas before reaching your agent. This ensures your Pydantic AI application never processes malformed classification models or unexpected category structures. If the API returns a tag that does not match your expected model, the system raises a validation error immediately. Catching mismatched types early keeps your automated pipelines safe and reliable.

Extract validated data schemas using this MCP Server.

The `extract_text` tool extracts structured entities and matches them against your typed Python models. Your Pydantic AI agent processes this validated output to update user profiles or trigger database writes safely. Using this tool guarantees that variables like email addresses or product numbers conform to your exact data types. You get clean runtime errors instead of corrupted database records.

Fetch active classifier metadata safely.

The `get_classifier_details` tool retrieves the exact configuration and label taxonomy of your active classification models. Your Pydantic AI agent uses this metadata to verify that its internal schemas match the remote configuration. This pre-flight check catches tag mismatches before your agent begins processing user text. Alerts trigger automatically if your team deploys a new tag on the dashboard.

Setup guide

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

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

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

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Common questions about MonkeyLearn MCP in Pydantic AI

Install the library via `pip install "pydantic-ai-slim[mcp]"` and instantiate `MCPToolset` with your Vinkius HTTP endpoint. Pass this toolset into the `toolsets` argument of your `Agent` constructor.

Yes, every payload returned by tools like `classify_text` is parsed and validated against internal Pydantic models. This ensures your agent only acts on structurally sound NLP data.

Yes, you can specify your custom model ID when calling `classify_text`. The tool fetches the corresponding tags, which Pydantic AI then parses into your typed structures.

Your agent can run `list_extractors` to retrieve a list of all active extraction models. This allows the system to choose the correct model dynamically based on the input text.

Your sensitive email text is sent over TLS directly to the API via our zero-trust gateway. The Vinkius sandbox operates entirely in memory and never writes any payload data to persistent storage.

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