How to Use the Speechnotes MCP in CrewAI

Q: How does CrewAI manage job progress with Speechnotes?

The crew can assign an agent to repeatedly call gettranscriptionstatus. This acts as a monitor loop, ensuring the workflow pauses until the required transcription result is available.

Q: Can I list my past Speechnotes jobs using CrewAI?

Yes. The 'Auditor' agent can use listtranscriptionhistory to gather all necessary records. This provides the team with a complete, actionable log of previous runs.

Q: What languages does Speechnotes support for CrewAI agents?

The 'Planner' agent should first call listsupportedlanguages. This confirms which language codes are accepted by the MCP Server before attempting any audio processing tasks.

Q: Is there a way to check Speechnotes connection health with CrewAI?

Yes. Running testspeechnotesauth is an excellent starting point for any crew's operational sequence, confirming that all agents can reach the MCP Server.

Speechnotes: Enable multi-agent teams for autonomous audio processing with CrewAI.

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Multi-Agent Transcription using Speechnotes

You can assign an 'Audio Specialist' agent to run the `transcribe_audio_url` tool. This allows the crew to process remote audio files autonomously without human intervention, triggering transcription and waiting for results. Another agent can then use `get_transcription_status` as a monitoring step, ensuring the first agent doesn't proceed until the MCP Server confirms job completion.

Speechnotes MCP Server for Audit and Reporting

A dedicated 'Auditor' agent can use `list_transcription_history` to gather all necessary records from previous runs. This keeps a detailed log of every job the crew ran, which is vital for compliance. The team needs visibility into costs; therefore, an 'Accountant' agent can call `get_usage_statistics` before starting any major project.

Controlling Data Flow with Speechnotes Webhooks

The crew doesn't need to poll constantly. Instead, set up webhooks and let the MCP Server notify your team when jobs are done. The 'Coordinator' agent can monitor `list_configured_webhooks` to know where notifications will land. This asynchronous approach is better for autonomous operations than having agents wait on synchronous API calls.

Setup guide

Set up Speechnotes MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Speechnotes tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Speechnotes Analyst",
    goal="Access and analyze Speechnotes data via MCP.",
    backstory="Expert analyst with direct Speechnotes access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Speechnotes transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Speechnotes Analyst",
        goal="Access and analyze Speechnotes data via MCP.",
        backstory="Expert analyst with direct Speechnotes access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Speechnotes transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Speechnotes. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Speechnotes MCP in CrewAI

The crew can assign an agent to repeatedly call `get_transcription_status`. This acts as a monitor loop, ensuring the workflow pauses until the required transcription result is available.

Yes. The 'Auditor' agent can use `list_transcription_history` to gather all necessary records. This provides the team with a complete, actionable log of previous runs.

The 'Planner' agent should first call `list_supported_languages`. This confirms which language codes are accepted by the MCP Server before attempting any audio processing tasks.

The server handles raw audio URLs, job status updates, and resulting text transcripts. The crew must ensure these specific content types are handled securely across all agents.

Yes. Running `test_speechnotes_auth` is an excellent starting point for any crew's operational sequence, confirming that all agents can reach the MCP Server.

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