How to Use the Writer (AI Enterprise LLM) MCP in CrewAI

Q: How do I implement RAG with Writer (AI Enterprise LLM) using CrewAI?

The research crew member handles this. They use uploadfile to ingest source documents, build the graph via creategraph, and then query it through askquestion for specialized answers.

Q: How does the MCP Server manage file access for CrewAI?

The server provides several tools: listfiles gives a paginated list of uploads. You can also get metadata on specific files using getfile, ensuring agents only interact with authorized data.

Run autonomous, multi-agent research and action using CrewAI with the Writer (AI Enterprise LLM) MCP Server.

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Connect Writer (AI Enterprise LLM) MCP to CrewAI

Create your Vinkius account to connect Writer (AI Enterprise LLM) 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 Writer (AI Enterprise LLM) with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Autonomous Data Research in CrewAI

The research agent can gather external facts by running `web_search`. Following that, another specialized agent uses these results to structure them, either by calling `add_file_to_graph` or generating a summary via `text_completion`.

Multi-Step Content Creation with CrewAI

When you need a final piece of content, the crew can use specialized generation tools. One agent might handle the initial draft using `chat_completion`, while another uses `translate_text` for localization before passing it to the moderator.

Advanced Document Analysis via MCP Server

Don't manually read documents. Assign a document analysis role to an agent. They can use `analyze_vision` on images or call `parse_pdf` to extract raw text from PDFs, passing it directly to another agent for structured querying.

Setup guide

Set up Writer (AI Enterprise LLM) 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 Writer (AI Enterprise LLM) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Writer (AI Enterprise LLM) 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 →

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="Writer (AI Enterprise LLM) Analyst",
        goal="Access and analyze Writer (AI Enterprise LLM) data via MCP.",
        backstory="Expert analyst with direct Writer (AI Enterprise LLM) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Writer (AI Enterprise LLM) 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 Writer. 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 Writer (AI Enterprise LLM) MCP in CrewAI

The research crew member handles this. They use `upload_file` to ingest source documents, build the graph via `create_graph`, and then query it through `ask_question` for specialized answers.

Yep. You can define an agent that calls application endpoints. It uses tools like `get_application`, executes the job with `generate_application_content_async`, and checks results with `list_application_jobs`.

The crew can handle this by having a dedicated translation agent. This agent calls the `translate_text` tool, ensuring that all outgoing content is properly localized before final review.

The server provides several tools: `list_files` gives a paginated list of uploads. You can also get metadata on specific files using `get_file`, ensuring agents only interact with authorized data.

It handles file content, which can be binary for Vision tasks or structured text. You'll see it dealing with uploaded files and generated application job results.

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