Can I update the status of a bug using the agent?

Yes! Use the `update_task` tool with the Project ID and Task ID. You can change the `status` (e.g., 'To Do', 'Doing', 'Done') or `priority` directly from your workspace.

How do I see the feedback queue for a specific project?

Simply ask the agent to `list_feedback` and provide the Project ID. It will retrieve all the tasks currently in the Feedback column, allowing you to triage them using natural language.

Does the integration allow creating a new project?

Yes. Use the `create_project` action and provide the project name and optionally a development URL. The project will be created in your BugHerd organization instantly.

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

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

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

Connect your BugHerd account to any AI agent and orchestrate your visual feedback, website bug tracking, and QA workflows through natural conversation.

Pydantic AI validates every BugHerd 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

Project Oversight — List all your active projects and retrieve detailed metadata, including development URLs.
Task & Bug Management — List all tasks in a project, retrieve detailed descriptions, and update statuses or priorities.
Feedback Processing — Access the dedicated feedback queue to triage new reports from your clients or team.
User Coordination — Access your directory of organization users and manage their involvement in projects.
Task Creation — Create new tasks or feedback reports directly from your workspace with descriptions and priority levels.
Organizational Insights — Retrieve core organization information and settings straight from your workspace.

The BugHerd 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 BugHerd to Pydantic AI via MCP

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

Why Use Pydantic AI with the BugHerd MCP Server

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

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

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

BugHerd + Pydantic AI Use Cases

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

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

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

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

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

BugHerd MCP Tools for Pydantic AI (10)

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

create_project

Create a new BugHerd project

create_task

Create a new task or feedback in a project

get_organization_info

Retrieve core organization settings

get_project

Get details of a specific project

get_task

Get details of a specific task

list_feedback

List tasks specifically in the Feedback queue

list_projects

List all BugHerd projects

list_tasks

List all tasks in a project

list_users

List all users in the organization

update_task

Update an existing task status or details

Example Prompts for BugHerd in Pydantic AI

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

"List all my active projects in BugHerd."

"Show the new feedback for the 'Vinkius Redesign' project."

"Update task task_123 in project proj_456 to status 'Doing'."

Troubleshooting BugHerd MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

BugHerd + Pydantic AI FAQ

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