Can I perform a quick search for a brand name?

Yes! Use the `post_search` action to trigger an immediate query. The tool will return a search hash, which you can then use with `get_mentions` to read the results.

How do I see the influencers for my tracked project?

Simply ask the agent to `get_influencers` and provide the Project ID. It will return the list of top voices mentioning your brand.

Does creating a search consume my account API credits?

Yes, standard API billing applies. You can always use the `get_remaining_credits` tool to check your quota before running large queries.

BrandMentions MCP Server for Pydantic AI 9 tools — connect in under 2 minutes

Name: BrandMentions
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

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

    result = await agent.run(
        "What tools are available in BrandMentions?"
    )
    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 BrandMentions MCP Server

Connect your BrandMentions social listening account to any AI agent and orchestrate your brand monitoring workflows through natural conversation.

Pydantic AI validates every BrandMentions tool response against typed schemas, catching data inconsistencies at build time. Connect 9 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

On-the-Spot Searches — Trigger immediate web and social media searches for specific keywords and retrieve the results instantly.
Campaign Management — List all your active tracking projects or create new ones to continuously monitor your brand or competitors.
Mention Auditing — Retrieve detailed mentions and sentiment analysis for your ongoing projects.
Influencer Discovery — List key influencers associated with your tracked keywords and projects.
Credit Tracking — Check your API limits and remaining credits in real-time.

The BrandMentions MCP Server exposes 9 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 BrandMentions to Pydantic AI via MCP

Follow these steps to integrate the BrandMentions 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 9 tools from BrandMentions with type-safe schemas

Why Use Pydantic AI with the BrandMentions MCP Server

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

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

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

BrandMentions + Pydantic AI Use Cases

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

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

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

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

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

BrandMentions MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect BrandMentions to Pydantic AI via MCP:

add_project

Create a new project for daily tracking

delete_project

Delete a project

get_influencers

List influencers for a specific project

get_mentions

Get full results for a completed search job

get_processed_mentions

Get partial results for a running search job

get_project_mentions

Retrieve mentions for a specific project

get_remaining_credits

Get current API credits limit/usage

list_projects

List all active campaigns/projects

post_search

Start an on-the-spot search job

Example Prompts for BrandMentions in Pydantic AI

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

"List all active tracking projects in BrandMentions."

"Start a quick search for the keyword 'Vinkius'."

"Show me the top influencers for project proj_1."

Troubleshooting BrandMentions MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

BrandMentions + Pydantic AI FAQ

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