Can I check the total play count for an episode using the agent?

Yes! Use the `get_episode` tool with the Episode ID. Your agent will fetch the detailed metadata, which includes the `total_plays` field for that specific episode.

How do I update the description of a podcast episode?

Simply ask the agent to `update_episode` and provide the Episode ID and the new description. The information will be synchronized with Buzzsprout immediately.

Does the integration allow uploading new audio files?

Currently, the toolset focuses on managing metadata, status, and analytics. Creating an episode via `create_episode` allows setting the title and description, but the audio file upload should be managed through the Buzzsprout web interface.

Buzzsprout MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Buzzsprout
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Buzzsprout through the Vinkius — pass the Edge URL in the `mcps` parameter and every Buzzsprout 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="Buzzsprout Specialist",
    goal="Help users interact with Buzzsprout effectively",
    backstory=(
        "You are an expert at leveraging Buzzsprout 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 Buzzsprout "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 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 Buzzsprout MCP Server

Connect your Buzzsprout account to any AI agent and orchestrate your podcast management, episode creation, and performance tracking through natural conversation.

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

What you can do

Episode Oversight — List all your podcast episodes and retrieve detailed metadata, including play counts and audio URLs.
Content Management — Create, update, or delete episodes directly from your workspace with custom titles and descriptions.
Performance Tracking — Monitor all-time play statistics for individual episodes to track your podcast growth.
Podcast Information — Retrieve core podcast details including artwork, website links, and categories.
Account Insights — Access your podcast configuration and settings straight from your workspace.
Deep Dives — Get detailed data for specific episode IDs using natural language.

The Buzzsprout MCP Server exposes 7 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 Buzzsprout to CrewAI via MCP

Follow these steps to integrate the Buzzsprout 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 7 tools from Buzzsprout

Why Use CrewAI with the Buzzsprout MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Buzzsprout 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 the 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

Buzzsprout + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Buzzsprout 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 Buzzsprout, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Buzzsprout 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 Buzzsprout against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Buzzsprout MCP Tools for CrewAI (7)

These 7 tools become available when you connect Buzzsprout to CrewAI via MCP:

create_episode

Create a new podcast episode

delete_episode

Delete an episode permanently

get_account_info

Retrieve core account/podcast settings

get_episode

Get details of a specific episode

get_podcast_info

Retrieve core podcast information

list_episodes

List all podcast episodes

update_episode

Update an existing episode

Example Prompts for Buzzsprout in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Buzzsprout immediately.

"List my last 5 podcast episodes in Buzzsprout."

"How many plays does the 'Tech Trends 2026' episode have?"

"Update the title of episode ep_123 to 'New Improved Title'."

Troubleshooting Buzzsprout MCP Server with CrewAI

Common issues when connecting Buzzsprout 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

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

Buzzsprout + CrewAI FAQ

Common questions about integrating Buzzsprout 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.