How do I find my Marchex Client ID and Secret?

Log in to the Marchex Developer Console and create an application to receive your credentials.

What call data can I access?

You can access call metadata, duration, caller info, status, and aggregated performance metrics.

Is my integration secure?

Absolutely. Your credentials are encrypted at rest and injected securely at runtime.

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

Name: Marchex
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 Marchex 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 Marchex "
            "(10 tools)."
        ),
    )

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

Connect your Marchex account to any AI agent and take full control of your conversation intelligence through natural conversation.

Pydantic AI validates every Marchex tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Call Tracking — Search for calls, fetch detailed metadata, and monitor statuses
Campaign Management — List and inspect tracking campaigns and their configurations
Number Management — Manage tracking phone numbers and account structures
Analytics — Retrieve aggregated performance metrics and call analytics

The Marchex 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 Marchex to Pydantic AI via MCP

Follow these steps to integrate the Marchex 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 Marchex with type-safe schemas

Why Use Pydantic AI with the Marchex MCP Server

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

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

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

Marchex + Pydantic AI Use Cases

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

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

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

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

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

Marchex MCP Tools for Pydantic AI (10)

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

get_account_details

Get details for a specific account

get_call_analytics

Get call analytics

get_call_details

Get details for a specific call

get_campaign_details

Get details for a specific campaign

get_number_details

Get details for a specific number

list_accounts

List all accounts

list_campaigns

List all campaigns

list_numbers

List all tracking numbers

list_users

List all users

search_calls

Search for phone calls

Example Prompts for Marchex in Pydantic AI

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

"Search for calls from last week."

"List active tracking campaigns."

"Show call analytics for today."

Troubleshooting Marchex MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Marchex + Pydantic AI FAQ

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