How do I get a DevSkiller API Key?

Log in to your DevSkiller account, navigate to **Settings > API**, and you can generate or retrieve your unique API Key from that section.

Can the agent show the candidate's code?

This integration currently focuses on retrieving scores, statuses, and technical reports. Full source code review should be performed within the DevSkiller platform.

How do I invite a candidate via chat?

You can use the invite_candidate_to_test tool. You will need to provide the candidate's email address and the specific Test ID from your catalog.

DevSkiller MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: DevSkiller
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to DevSkiller through Vinkius, pass the Edge URL in the `mcps` parameter and every DevSkiller 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="DevSkiller Specialist",
    goal="Help users interact with DevSkiller effectively",
    backstory=(
        "You are an expert at leveraging DevSkiller 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 DevSkiller "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 DevSkiller MCP Server

Integrate DevSkiller, the technical screening and talent assessment platform, directly into your AI workflow. Manage your candidate pipeline, send test invitations, and retrieve detailed assessment reports and skill scores using natural language.

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

What you can do

Candidate Oversight — List and search for candidates in your database and monitor their current assessment status.
Test Management — Access your library of available technical tests, coding tasks, and quizzes.
Invitation Tracking — Monitor sent test invitations and track candidate progress in real-time.
Performance Analytics — Retrieve full assessment reports with granular skill scores and performance metrics.

The DevSkiller MCP Server exposes 10 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 DevSkiller to CrewAI via MCP

Follow these steps to integrate the DevSkiller 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 10 tools from DevSkiller

Why Use CrewAI with the DevSkiller MCP Server

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

DevSkiller + CrewAI Use Cases

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

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

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

DevSkiller MCP Tools for CrewAI (10)

These 10 tools become available when you connect DevSkiller to CrewAI via MCP:

get_account_metadata

Retrieve metadata and limits for your DevSkiller account

get_candidate_assessment_report

Retrieve the full assessment report for a candidate

get_candidate_profile

Get detailed information for a specific candidate

invite_candidate_to_test

Send a new test invitation to a candidate

list_assessment_candidates

List all candidates in your DevSkiller account

list_available_tests

List all assessment tests configured in your catalog

list_high_score_candidates

Identify candidates who achieved a score above a specific threshold

list_recently_sent_invitations

List test invitations sent in the last 24 hours

list_test_invitations

List all sent test invitations and their current status

search_candidates_by_identity

Search for a candidate by name or email keyword

Example Prompts for DevSkiller in CrewAI

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

"List all candidates who scored above 85% in recent tests."

"Show me the assessment status for candidate 'john.doe@example.com'."

"Invite 'Sarah Smith' (sarah@example.com) to the 'Frontend React' test."

Troubleshooting DevSkiller MCP Server with CrewAI

Common issues when connecting DevSkiller 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

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

DevSkiller + CrewAI FAQ

Common questions about integrating DevSkiller 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.