How to Use the N-Gram Frequency Engine MCP in Pydantic AI

Q: What happens if my text input to the tool contains non-ASCII characters?

The extractngramfrequencies tool uses standard UTF-8 string processing to split words. It normalizes text to lowercase and strips punctuation, returning accurate counts for standard alphabetic scripts.

Q: How does the server secure the raw text strings sent by my agent?

All text payloads submitted to extractngramfrequencies are processed in-memory within an isolated V8 sandbox. No raw text is written to disk or stored, and the temporary execution memory is wiped immediately after returning the counts.

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Connect N-Gram Frequency Engine MCP to Pydantic AI

Create your Vinkius account to connect N-Gram Frequency Engine 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 N-Gram Frequency Engine with Pydantic AI

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Type-safe frequency counts for Pydantic AI agents

The `extract_ngram_frequencies` tool returns strictly typed JSON arrays directly to your agent, preventing silent parsing failures. Because Pydantic AI enforces runtime validation, the returned unigram, bigram, and trigram counts are verified against your schema before your code ever touches them. You connect your agent using the unified `MCPToolset` constructor pointing to the external server URL. The agent invokes the deterministic parser, receives the structured frequency map, and immediately validates the fields, raising loud errors if any data anomalies occur.

Avoid silent model corruption with this MCP Server

The `extract_ngram_frequencies` tool eliminates the risk of hallucinated word counts by processing raw text outside of the LLM context. Your agent uses the tool to get exact counts, ensuring your downstream logic operates on actual linguistic data rather than guessed numbers. Using this MCP Server allows your Pydantic AI agents to remain model-agnostic. Whether you run your agent on a local model or a commercial API, the underlying text analysis remains perfectly consistent and strictly typed.

Stream counts over SSE and HTTP transports

The `extract_ngram_frequencies` tool is built to handle massive text inputs over both Streamable HTTP and SSE transports. Your agent can offload heavy string normalization and tokenization to the external server, keeping your main Python process lightweight. You install the slim client package and register the toolset directly in the agent constructor. This setup ensures that your agent can process huge corpora and receive structured frequency data without loading heavy NLP libraries into your local runtime.

Setup guide

Set up N-Gram Frequency Engine 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": {
        "n-gram-frequency-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent N-Gram Frequency Engine transactions")
print(result.output)

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Common questions about N-Gram Frequency Engine MCP in Pydantic AI

Install the slim package with MCP support, then initialize the connection using `MCPToolset` with your HTTP or SSE endpoint. Pass this toolset directly to the `Agent` constructor to expose the `extract_ngram_frequencies` tool.

Yes, the tool returns a structured JSON payload that is automatically validated against your Pydantic models at runtime. If the `extract_ngram_frequencies` output deviates from the expected schema, the framework immediately raises a validation error.

Yes, this MCP Server must run externally when using this framework. You connect your agent using the unified `MCPToolset` pointing to the running server's HTTP or SSE transport address.

The `extract_ngram_frequencies` tool uses standard UTF-8 string processing to split words. It normalizes text to lowercase and strips punctuation, returning accurate counts for standard alphabetic scripts.

All text payloads submitted to `extract_ngram_frequencies` are processed in-memory within an isolated V8 sandbox. No raw text is written to disk or stored, and the temporary execution memory is wiped immediately after returning the counts.

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