How to Use the AI Token Counter MCP in Pydantic AI

Ensure type-safe context management with the AI Token Counter MCP for Pydantic AI. Build agents that fail loudly before they crash.

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Connect AI Token Counter MCP to Pydantic AI

Create your Vinkius account to connect AI Token Counter 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 AI Token Counter with Pydantic AI

Ask AI about this MCP

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Perplexity

Strict token validation

Your agent uses `count_tokens` to verify context size before any API call. This fits perfectly into your validation-first workflow. If the count exceeds your limits, your agent catches it immediately. You avoid the mess of partial responses and corrupted data.

Type-safe resource management

Every token count is treated as a critical piece of data within your Pydantic AI agent. It allows you to enforce schema constraints on your context memory. This prevents the agent from hallucinating fields or failing during deserialization. You get a robust way to handle context limits.

Tamper-proof agent audit logs

Every time your agent counts tokens, Vinkius generates a signed audit trail. You can verify exactly what your agent saw and when it saw it. This level of detail is essential for debugging complex agent behaviors. You have a verifiable history of every decision made.

Setup guide

Set up AI Token Counter 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": {
        "ai-token-counter-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent AI Token Counter transactions")
print(result.output)

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