Bring Infrastructure As Code
to CrewAI
Learn how to connect Terraform Cloud (HCP) to CrewAI and start using 42 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Terraform Cloud (HCP) MCP Server?
Connect your Terraform Cloud (HCP) account to any AI agent to orchestrate your Infrastructure as Code (IaC) workflows through natural language. This server provides comprehensive access to the HCP Terraform API, allowing for seamless management of the entire infrastructure lifecycle.
What you can do
- Organization & Project Management — List, create, and inspect organizations and projects to maintain high-level governance.
- Workspace Operations — Query workspaces, manage locks, and configure VCS integrations for automated deployments.
- Run & Plan Lifecycle — Trigger new runs, apply or discard plans, and monitor the progress of infrastructure changes in real-time.
- State & Outputs — Retrieve current state versions and extract specific output values to use in downstream automation or analysis.
- Governance & Security — Manage teams, access controls, variable sets, and Sentinel/OPA policies directly via the agent.
How it works
- Subscribe to this server
- Enter your Terraform Cloud User or Team API Token
- Start managing your cloud environments from Claude, Cursor, or any MCP-compatible client
Who is this for?
- DevOps & Platform Engineers — automate routine workspace management and run monitoring without leaving the terminal or chat.
- Cloud Architects — quickly inspect state outputs and policy compliance across multiple organizations.
- SRE Teams — troubleshoot failed runs and manage workspace locks during incident response.
Built-in capabilities (42)
Add a user to a team
Grant a team access to a workspace
Apply a planned run
Apply a variable set to a workspace
Associate a run task with a workspace
Cancel a run
Create a notification configuration for a workspace
Create a new organization
Create a policy
Create a policy set
Create a new project
Create a private registry module (No VCS)
Create a private registry provider
Create a new run (plan/apply)
Create a run task
Create a new state version
Create a new team
Create a variable set
Create a new workspace
Create a variable in a workspace
Destroy an organization
Discard a run
Execute an explorer query across workspaces
Force unlock a workspace
Get current state version for a workspace
Get JSON execution plan output
Get outputs for a state version
List organization audit events
List HCP Terraform organizations
List projects in an organization
List teams in an organization
List workspaces in an organization
Lock a workspace
Remove a user from a team
Safe delete a workspace
Show details of an apply
Show details of a specific organization
Show details of a plan
Unlock a workspace
Update an existing organization
Update a team
Upload code for a policy
Why CrewAI?
When paired with CrewAI, Terraform Cloud (HCP) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Terraform Cloud (HCP) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
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
mcpsparameter 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
Terraform Cloud (HCP) in CrewAI
Terraform Cloud (HCP) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Terraform Cloud (HCP) to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Terraform Cloud (HCP) in CrewAI
The Terraform Cloud (HCP) 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. All 42 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Terraform Cloud (HCP) for CrewAI
Every tool call from CrewAI to the Terraform Cloud (HCP) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I trigger a new infrastructure deployment (run) using this server?
Yes. You can use the create_run tool by providing the Workspace ID. You can also subsequently use apply_run or discard_run to manage the lifecycle of that specific execution.
How do I see the output variables from my last successful Terraform apply?
Use the get_state_version_outputs tool with the Workspace ID. It will retrieve all calculated outputs from the current state, such as IP addresses, DNS names, or resource IDs.
Is it possible to list all workspaces across my organization?
Absolutely. Use the list_workspaces tool and provide your organization name. You can also filter the results by name or tags using optional parameters.
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.
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.
Explore More MCP Servers
View all →
Octoboard
12 toolsBuild client-facing marketing reports automatically by pulling data from Google, Facebook, HubSpot, and 50 other integrations.
Impact.com
10 toolsManage partnership campaigns, ads, and affiliate payouts via Impact.com API.
Incident.io
10 toolsManage incidents, roles, and on-call schedules via Incident.io API.
Jamf Pro
10 toolsManage Apple devices, computers, and inventory via Jamf Pro API.