How to Use the GitScrum Knowledge MCP in CrewAI

Q: Can a CrewAI agent perform a global search using GitScrum Knowledge?

Yes, you include the globalsearch tool in your agent's definition. The agent uses this to pull relevant information from notes, wikis, and messages simultaneously.

Q: How do CrewAI agents share GitScrum Knowledge notes across the crew?

They use listnotes to access the shared memory. Any agent can create or update a note, making the information available to the rest of the crew.

Q: Is it possible for CrewAI to manage wiki pages via GitScrum Knowledge?

It is. You give the crew tools like createwikipage and updatewikipage. They can manage your documentation hierarchy based on the tasks you assign.

Deploy a specialized agent crew to maintain your GitScrum Knowledge workspace autonomously.

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Connect GitScrum Knowledge MCP to CrewAI

Create your Vinkius account to connect GitScrum Knowledge to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup GitScrum Knowledge with CrewAI

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Autonomous documentation maintenance

Assign a dedicated agent to monitor and update wiki pages using `update_wiki_page`. It ensures your documentation stays in sync with your latest project status. Your crew can handle structural changes by using `list_wiki_pages` to audit the existing tree. It identifies missing sections and generates new content as needed.

Agent memory synchronization

Each agent in your crew shares a common memory store via `list_notes` and `create_note`. They build a collective knowledge base that informs their decision-making process. When a decision is finalized, the crew uses `toggle_note_share` to expose the result to the team. This ensures transparency across your autonomous operations.

Collaborative project orchestration

Your crew uses `list_discussions` and `search_channel_messages` to gather context from the team. It allows them to act on real-time human input. They report findings back by using `send_message`. This creates a closed-loop system where agents and humans trade information without manual intervention.

Setup guide

Set up GitScrum Knowledge 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 GitScrum Knowledge tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent GitScrum Knowledge 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="GitScrum Knowledge Analyst",
        goal="Access and analyze GitScrum Knowledge data via MCP.",
        backstory="Expert analyst with direct GitScrum Knowledge access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent GitScrum Knowledge 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 GitScrum. 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 GitScrum Knowledge MCP in CrewAI

Yes, you include the `global_search` tool in your agent's definition. The agent uses this to pull relevant information from notes, wikis, and messages simultaneously.

They use `list_notes` to access the shared memory. Any agent can create or update a note, making the information available to the rest of the crew.

It is. You give the crew tools like `create_wiki_page` and `update_wiki_page`. They can manage your documentation hierarchy based on the tasks you assign.

You apply a tool filter in your MCP configuration. This prevents specific agents from accessing sensitive tools like `delete_wiki_page` or `delete_note`.

The server requires an endpoint token for every request. This ensures that your workspace notes and channel history are only accessed by your defined crew.

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