Can I retrieve social mentions for a specific date range?

Yes! Use the `get_mentions` tool with the Project ID, Query ID, and your ISO 8601 formatted start and end dates (e.g., 2024-01-01T00:00:00.000Z).

How do I see the volume trend of a query over time?

Simply ask the agent to `get_volume_aggregates` and provide the relevant IDs and date range. It will return the aggregated data points showing mention spikes.

Does the integration allow creating new boolean queries?

Currently, the toolset is focused on reading and retrieving data from existing setups. Creating complex boolean queries should be done via the Brandwatch web interface to utilize their query builder and syntax validation.

Brandwatch MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Brandwatch
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Brandwatch through 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 Brandwatch "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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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 Brandwatch MCP Server

Connect your Brandwatch Consumer Research account to any AI agent and orchestrate your social listening and data analysis workflows through natural conversation.

Pydantic AI validates every Brandwatch tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through 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

Project & Dashboard Navigation — List and retrieve detailed metadata for all your active research projects and dashboards.
Query Management — Access your configured search queries to monitor brand health and industry trends.
Mention Retrieval — Query and inspect raw social mentions based on specific queries and date ranges.
Data Aggregation — Retrieve volume aggregates to analyze mention trends and spikes over time.
Tag Coordination — List and create categorization tags to organize your social data effectively.

The Brandwatch MCP Server exposes 8 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 Brandwatch to Pydantic AI via MCP

Follow these steps to integrate the Brandwatch 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 8 tools from Brandwatch with type-safe schemas

Why Use Pydantic AI with the Brandwatch MCP Server

Pydantic AI provides unique advantages when paired with Brandwatch 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 Brandwatch integration code

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

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

Brandwatch + Pydantic AI Use Cases

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

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

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

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

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

Brandwatch MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Brandwatch to Pydantic AI via MCP:

create_tag

Create a new tag for categorizing mentions

get_mentions

Retrieve mentions for a specific query

get_project

Get details of a specific project

get_volume_aggregates

Get mention volume aggregates for a query

list_dashboards

List dashboards in a project

list_projects

List all active projects

list_queries

List configured queries in a project

list_tags

List tags available in a project

Example Prompts for Brandwatch in Pydantic AI

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

"List all queries configured in project proj_1."

"Get volume aggregates for query q_1 from Jan 1st to Jan 31st."

"Create a new tag called 'Urgent Review' in project proj_1."

Troubleshooting Brandwatch MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Brandwatch + Pydantic AI FAQ

Common questions about integrating Brandwatch 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 Brandwatch MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.