How do I find my ApplicantStack API Token?

Log in to ApplicantStack, go to **Settings**, then **Edit Settings**. Your API token will be listed under the API section.

What is the subdomain?

The subdomain is the first part of your ApplicantStack URL (e.g., if your URL is `mycompany.applicantstack.com`, your subdomain is `mycompany`).

Can I move a candidate to a new stage?

Yes, use the `update_candidate` tool and provide the new stage name in the `stage` field to advance them in your workflow.

ApplicantStack MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: ApplicantStack
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

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

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="ApplicantStack Specialist",
    goal="Help users interact with ApplicantStack effectively",
    backstory=(
        "You are an expert at leveraging ApplicantStack 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 ApplicantStack "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

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

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* 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 ApplicantStack MCP Server

The ApplicantStack MCP Server integrates your recruiting and onboarding workflows directly into your AI workspace. Efficiently manage your job listings, track candidate progress through custom stages, and streamline your hiring process using simple natural language.

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

Key Features

Job Management — List all active and closed job openings, and retrieve full metadata for any specific listing.
Candidate Tracking — Access your entire applicant database and filter by workflow stage or score.
Workflow Automation — Move candidates between stages (e.g., from 'Interview' to 'Hired') and update their profiles instantly.
Onboarding & Hires — Access onboarding data for new hires to ensure a smooth transition from applicant to employee.
Secure Access — Uses private access tokens to safely interact with your organization's recruiting data.

Benefits for Teams

Recruiters — Quickly check the status of candidates for multiple jobs without switching between tabs.
Hiring Managers — Review candidate profiles and scores using AI-assisted summaries.
HR Teams — Track hiring trends and ensure onboarding tasks are initiated for all new hires.

The ApplicantStack MCP Server exposes 7 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 ApplicantStack to CrewAI via MCP

Follow these steps to integrate the ApplicantStack 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 7 tools from ApplicantStack

Why Use CrewAI with the ApplicantStack MCP Server

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

ApplicantStack + CrewAI Use Cases

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

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

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

ApplicantStack MCP Tools for CrewAI (7)

These 7 tools become available when you connect ApplicantStack to CrewAI via MCP:

get_account_check

Verify ApplicantStack account connection

get_candidate

Get details for a specific candidate

get_job

Get details for a specific job

list_candidates

List all candidates

list_hires

List all hires (onboarding)

list_jobs

List all job listings in ApplicantStack

update_candidate

Use stage field to move them in the workflow. Update candidate information or stage

Example Prompts for ApplicantStack in CrewAI

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

"List all active job openings in ApplicantStack."

"Show me candidates currently in the 'Interview' stage."

"Move candidate 'C12345' to the 'Hired' stage."

Troubleshooting ApplicantStack MCP Server with CrewAI

Common issues when connecting ApplicantStack 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.

ApplicantStack + CrewAI FAQ

Common questions about integrating ApplicantStack 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.