How do I get a Determ API Key?

Log in to your Determ account, navigate to **Settings > API**, and you can retrieve your unique API Key from that section.

Can the agent show the full text of an article?

Yes, you can use the get_mention_details tool to retrieve the full content and metadata for a specific mention found by Determ.

Is real-time monitoring supported?

Yes, the list_media_mentions tool retrieves the most recent mentions based on your query settings, providing real-time data from the Determ platform.

Determ MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Determ
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Determ through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Determ "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Determ?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Determ MCP Server

Integrate Determ (formerly Mediatoolkit), the powerful media monitoring and social listening platform, directly into your AI workflow. Track brand mentions across the web and social media, analyze sentiment trends, and monitor your competitive landscape using natural language.

Pydantic AI validates every Determ tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Mention Monitoring — List and retrieve real-time media mentions for your keywords and topics from over 100 million sources.
Sentiment Intelligence — Retrieve a breakdown of sentiment (positive, neutral, negative) for any of your monitoring queries.
Query Management — List and review your configured monitoring queries and their specific settings.
Analytics Reporting — Access metadata for your media monitoring and analytics reports directly via chat.

The Determ MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Determ to Pydantic AI via MCP

Follow these steps to integrate the Determ MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Determ with type-safe schemas

Why Use Pydantic AI with the Determ MCP Server

Pydantic AI provides unique advantages when paired with Determ through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Determ integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Determ connection logic from agent behavior for testable, maintainable code

Determ + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Determ MCP Server delivers measurable value.

Type-safe data pipelines: query Determ with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Determ tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Determ and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Determ responses and write comprehensive agent tests

Determ MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Determ to Pydantic AI via MCP:

get_account_metadata

Retrieve settings and limits for your Determ account

get_mention_details

Get full content and technical metadata for a specific media mention

get_monitoring_query_details

Get detailed settings and status for a specific monitoring query

get_query_sentiment_summary

Retrieve a breakdown of sentiment (positive, neutral, negative) for a specific query

list_analytics_reports

List all available analytics and media monitoring reports

list_media_mentions

List recent media mentions for a specific monitoring query

list_monitoring_queries

List all media monitoring queries (keywords/topics) in your Determ account

list_recent_high_reach_mentions

List only the mentions with the highest estimated reach

list_top_media_sources

Identify the media sources with the highest volume of mentions (mock logic)

search_mentions_by_keyword

Search for specific keywords within the mentions of a monitoring query

Example Prompts for Determ in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Determ immediately.

"List my active monitoring queries."

"Show me the sentiment breakdown for the 'Main Competitor' query."

"What are the top media sources for 'Industry Trends'?"

Troubleshooting Determ MCP Server with Pydantic AI

Common issues when connecting Determ to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Determ + Pydantic AI FAQ

Common questions about integrating Determ MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Determ MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.