Alpic MCP Server for CrewAI 18 tools — connect in under 2 minutes
Connect your CrewAI agents to Alpic through Vinkius, pass the Edge URL in the `mcps` parameter and every Alpic tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="Alpic Specialist",
goal="Help users interact with Alpic effectively",
backstory=(
"You are an expert at leveraging Alpic 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 Alpic "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 18 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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 Alpic MCP Server
What you can do
Connect AI agents to the Alpic platform for complete MCP server lifecycle management:
When paired with CrewAI, Alpic becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Alpic tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- List and manage teams with member access controls
- Create, update, and delete MCP server projects with git repository linking
- Deploy to multiple environments (dev, staging, production) with one command
- Monitor deployments with real-time status, logs, and analytics
- Manage environment variables securely for each deployment target
- View analytics including request counts, latency, error rates, and usage patterns
- Publish to the MCP registry to make your servers discoverable
- Create development tunnels for local testing before production deployment
The Alpic MCP Server exposes 18 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 Alpic to CrewAI via MCP
Follow these steps to integrate the Alpic 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 18 tools from Alpic
Why Use CrewAI with the Alpic MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Alpic 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
Alpic + CrewAI Use Cases
Practical scenarios where CrewAI combined with the Alpic MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries Alpic 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 Alpic, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain Alpic 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 Alpic against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
Alpic MCP Tools for CrewAI (18)
These 18 tools become available when you connect Alpic to CrewAI via MCP:
add_variable
Use this to set API keys, database URLs, feature flags, or any configuration needed by your MCP server. Requires project ID, environment ID, variable key, and value. Variable values are stored securely. Add a new environment variable to an Alpic environment
create_environment
Requires environment name and project ID. Optionally set initial variables and configuration. Each environment gets a unique URL for MCP client connections. Returns the created environment details. Create a new deployment environment (dev, staging, prod) for an Alpic project
create_project
Requires project name and team ID. Optionally set description, repository URL, and initial configuration. Returns the created project details including the new project ID needed for subsequent operations. Create a new MCP server project in Alpic
delete_project
This action cannot be undone. Use with caution. Requires the project ID. Confirm with the user before proceeding. Delete an Alpic MCP server project
delete_variable
Use this to clean up unused configuration keys. Requires project ID, environment ID, and variable key. Delete an environment variable from an Alpic environment
deploy_environment
The deployment runs asynchronously. Returns the deployment ID which can be used with get_deployment to check status. Use this to push new MCP server versions to dev, staging, or production environments. Trigger a new deployment for a specific Alpic environment
get_deployment
Requires the deployment ID. Use this to check if a deployment succeeded, review deployment history, or debug failed deployments. Get detailed status and metadata for a specific Alpic deployment
get_deployment_logs
Useful for debugging failed deployments, understanding build output, or verifying successful startup of the MCP server. Requires project ID and environment ID. Get deployment logs for a specific Alpic environment
get_project
Requires the project ID from list_projects results. Use this to review project settings before making updates or triggering deployments. Get detailed information about a specific Alpic MCP server project
get_project_analytics
Requires the project ID. Use this to monitor MCP server health, identify performance trends, and troubleshoot issues. Get analytics and usage data for a specific Alpic project
get_server_info
Use this to verify which MCP tools are exposed and confirm the server is running correctly. Get server information and status for a specific Alpic project
get_tunnel_ticket
Returns the tunnel URL and ticket token. Use this during development to test your MCP server before deploying to a production environment. Get a tunnel ticket for local development and testing of an MCP server
list_environments
Each environment has its own URL, variables, and deployment status. Returns environment IDs, names, URLs, and current deployment state. Use this to identify which environment to deploy to or manage variables for. List all environments (dev, staging, prod) for a specific Alpic project
list_projects
Returns project IDs, names, descriptions, associated teams, deployment status, and environment counts. Use this to overview your entire MCP infrastructure before managing specific projects or triggering deployments. List all MCP server projects in your Alpic account
list_teams
Each team contains projects and environments for deploying MCP servers. Returns team IDs, names, and member counts. Use this first to identify which team to manage projects under. List all teams associated with your Alpic account
list_variables
Variable values are masked for security. Returns variable keys and metadata. Use this to audit environment configuration before deploying or adding new variables. List all environment variables configured for an Alpic environment
publish_to_registry
Requires project ID and optionally a server description and category. Use this to make your MCP server publicly available. Publish an MCP server to the official MCP registry via Alpic
update_project
Only pass the fields you want to change. Requires the project ID from list_projects results. Use this to rename projects, update descriptions, or point to a new repository branch. Update an existing Alpic MCP server project configuration
Example Prompts for Alpic in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with Alpic immediately.
"List all active Alpic projects running on my account natively, then check the error rate metric for the first one listed."
"Deploy the staging environment for our main enterprise project mapped on isolated branches."
"Audit the credentials in our production environment. Provide exact details of variable schemas missing from active lists."
Troubleshooting Alpic MCP Server with CrewAI
Common issues when connecting Alpic to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
Alpic + CrewAI FAQ
Common questions about integrating Alpic MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
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?
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?
Can CrewAI agents call multiple MCP tools in parallel?
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)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect Alpic with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Alpic to CrewAI
Get your token, paste the configuration, and start using 18 tools in under 2 minutes. No API key management needed.