How to Use the DevDocs MCP in CrewAI

Q: How do I configure the DevDocs MCP Server for CrewAI?

Run pip install crewai "crewaitools". You can pass the endpoint URL directly into the mcps array on your agent definition for instant access.

Q: Can I restrict DevDocs MCP tools to specific CrewAI agents?

Yes. Import MCPServerHTTP from crewai.mcp and apply a toolfilter. You give the search tool to your researcher and the read tool to your analyzer.

Equip your CrewAI research teams with direct access to complete developer documentation and API references.

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Connect DevDocs MCP to CrewAI

Create your Vinkius account to connect DevDocs 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 DevDocs with CrewAI

Ask AI about this MCP

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Claude

Perplexity

CrewAI MCP Server Role Assignment

The `list_libraries` tool lets your scout agent verify supported frameworks before handing out tasks. You designate one specific worker to map the available documentation targets. That scout writes its findings straight into shared memory. The rest of the crew knows exactly which SDKs to query without burning execution cycles on dead ends.

Sequential Documentation Hunting

The `search_docs` tool allows an analyst agent to find exact manual page paths based on natural language queries. The analyst scans the index and extracts the precise routing strings needed to fetch the actual text. This creates a rigid sequential execution chain. The analyst finds the path, halts, and passes that exact string to the next agent down the line.

Autonomous Markdown Extraction

The `read_page` tool pulls cleanly formatted Markdown text into the active session. It ignores the navigation menus and grabs only the technical specs. Your coding agent consumes this raw text to write working implementations. The squad collaborates to research and apply the documentation without you lifting a finger.

Setup guide

Set up DevDocs 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 DevDocs tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Run `pip install crewai "crewai[tools]"`. You can pass the endpoint URL directly into the `mcps` array on your agent definition for instant access.

Yes. Import `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. You give the search tool to your researcher and the read tool to your analyzer.

A manager agent can coordinate the documentation lookup. It delegates the library verification to a subordinate, waits for the result, and then orders the search operation.

The tool returns an empty result. The agent reads the failure from shared memory and pivots its strategy, usually by asking the manager for a different framework to investigate.

Your agents send framework names and search keywords to the Vinkius endpoints. We spin up a zero-trust sandbox to fetch the payload, return the Markdown, and instantly destroy the container. We never persist your architecture research.

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