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 CrewAI 10 tools — connect in under 2 minutes

Name: BugHerd
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to BugHerd through Vinkius, pass the Edge URL in the `mcps` parameter and every BugHerd tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="BugHerd Specialist",
    goal="Help users interact with BugHerd effectively",
    backstory=(
        "You are an expert at leveraging BugHerd tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in BugHerd "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* 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.

When paired with CrewAI, BugHerd becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call BugHerd tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the BugHerd MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from BugHerd

Why Use CrewAI with the BugHerd MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with BugHerd through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

BugHerd + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries BugHerd for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries BugHerd, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain BugHerd tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries BugHerd against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

BugHerd MCP Tools for CrewAI (10)

These 10 tools become available when you connect BugHerd to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

BugHerd + CrewAI FAQ

Common questions about integrating BugHerd MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.