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HashiCorp Terraform

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The Terraform MCP Server provides seamless integration with Terraform ecosystem, enabling advanced automation and interaction capabilities for Infrastructure as Code (IaC) development.

Publisherhashicorp
Repositoryterraform-mcp-server
LanguageGo
Forks
198
Stars
1.5K
Available tools
16
Transport typestdio
Categories
LicenseMPL-2.0
Links
  • Connect tools to AI workflows

    HashiCorp Terraform exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 16 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    1.5K stars and 198 forks from the linked repository.

Terraform MCP Server

The Terraform MCP Server is a Model Context Protocol (MCP) server that integrates seamlessly with Terraform Registry and HCP Terraform APIs, enabling advanced automation and interaction capabilities for Infrastructure as Code (IaC) development.

Table of Contents

Features

  • Dual Transport Support: Both Stdio and StreamableHTTP transports with configurable endpoints
  • Terraform Registry Integration: Direct integration with public Terraform Registry APIs for providers, modules, and policies
  • HCP Terraform & Terraform Enterprise Support: Full workspace management, organization/project listing, and private registry access
  • Workspace Operations: Create, update, delete workspaces with support for variables, tags, and run management
  • OTel metrics for monitoring tool usage: Integration with open telemetry meters to track tool-call volume, latency and failures in Streamable HTTP mode. Also exposes default http server metrics when this feature is enabled

Security Note: Depending on the query, the MCP server may expose certain Terraform data to the MCP client and LLM. Do not use the MCP server with untrusted MCP clients or LLMs.

Legal Note: Your use of a third party MCP Client/LLM is subject solely to the terms of use for such MCP/LLM, and IBM is not responsible for the performance of such third party tools. IBM expressly disclaims any and all warranties and liability for third party MCP Clients/LLMs, and may not be able to provide support to resolve issues which are caused by the third party tools.

Caution: The outputs and recommendations provided by the MCP server are generated dynamically and may vary based on the query, model, and the connected MCP client. Users should thoroughly review all outputs/recommendations to ensure they align with their organization’s security best practices, cost-efficiency goals, and compliance requirements before implementation.

Prerequisites

  1. Ensure Docker is installed and running to use the server in a containerized environment.
  2. Install an AI assistant that supports the Model Context Protocol (MCP).

Command Line Options

Environment Variables:

VariableDescriptionDefault
TFE_ADDRESSSets the Terraform Enterprise/HCP Terraform address for API calls. Must include the protocol (e.g., https://app.terraform.io). In streamable-http mode this is the only way to set the address; it cannot be supplied by clients via header or query parameter.Optional
TFE_TOKENTerraform Enterprise API token"" (empty)
TF_MCP_SHARED_SECRETShared secret sent as the X-Tf-Mcp-Secret header on requests to HCP Terraform / TFE, used to identify requests originating from a hosted MCP deployment. Should only be used over TLS."" (empty)
TFE_SKIP_TLS_VERIFYSkip HCP Terraform or Terraform Enterprise TLS verificationfalse
LOG_LEVELLogging level: trace, debug, info, warn, error, fatal, panic (overrides --log-level flag)info
LOG_FORMATLogging format: text or json (overrides --log-format flag)text
TRANSPORT_MODESet to streamable-http to enable HTTP transport (legacy http value still supported)stdio
TRANSPORT_HOSTHost to bind the HTTP server127.0.0.1
TRANSPORT_PORTHTTP server port8080
MCP_ENDPOINTHTTP server endpoint path/mcp
MCP_REDIRECT_ROOT_URLURL to redirect requests to / to""
MCP_KEEP_ALIVEKeep-alive interval for SSE connections (e.g., 30s, 1m). 0 to disable0
MCP_SESSION_MODESession mode: stateful or statelessstateful
MCP_ALLOWED_ORIGINSComma-separated list of allowed origins for CORS"" (empty)
MCP_CORS_MODECORS mode: strict, development, or disabledstrict
MCP_TLS_CERT_FILEPath to TLS cert file, required for non-localhost deployment (e.g. /path/to/cert.pem)"" (empty)
MCP_TLS_KEY_FILEPath to TLS key file, required for non-localhost deployment (e.g. /path/to/key.pem)"" (empty)
MCP_RATE_LIMIT_GLOBALGlobal rate limit (format: rps:burst)10:20
MCP_RATE_LIMIT_SESSIONPer-session rate limit (format: rps:burst)5:10
MCP_ORGANIZATION_ALLOWLISTCSV list of HCP Terraform organization names allowed to access the HTTP server"" (empty)
MCP_FORWARD_CLIENT_IPForward the client IP to HCP Terraform / TFE via X-Forwarded-For. Set to true to enablefalse
MCP_REMOTE_IP_METHODHow the client IP is sourced when forwarding is enabled: RemoteAddr (direct connection only), X-Real-IP, or X-Forwarded-ForRemoteAddr
MCP_XFF_TRUSTED_HOPSNumber of trusted proxy hops counted from the right of the X-Forwarded-For chain. Only used when MCP_REMOTE_IP_METHOD=X-Forwarded-For0
ENABLE_TF_OPERATIONSEnable tools that require explicit approvalfalse
OTEL_METRICS_ENABLEDEnable tools and server metrics using otelfalse
OTEL_METRICS_SERVICE_VERSIONVersion of the terraform-mcp-server sending metrics, which is used to set metric attributes. It also helps track metrics across different deploymentslatest
OTEL_METRICS_SERVICE_NAMEIdentifies the source of the metrics (e.g., "terraform-mcp-server")terraform-mcp-server
OTEL_METRICS_EXPORT_INTERVALControls the frequency of metric flushes2
OTEL_METRICS_ENDPOINTURL of your OTel Collector or backendlocalhost:4318
INSTANA_ENABLEDEnable Instana instrumentation (metrics and HTTP request tracing) for the streamable-http server. Requires an Instana agent that is reachable by the server.false
INSTANA_SERVICE_NAMEIf Instana instrumentation is enabled, the service name to use for the MCP serverterraform-mcp-server
bash
# Stdio mode
terraform-mcp-server stdio [--log-file /path/to/log] [--log-level info] [--log-format text] [--toolsets <toolsets>] [--tools <tools>]

# StreamableHTTP mode
terraform-mcp-server streamable-http [--transport-port 8080] [--transport-host 127.0.0.1] [--mcp-endpoint /mcp] [--organization-allowlist <orgs-csv>] [--log-file /path/to/log] [--log-level info] [--log-format text] [--toolsets <toolsets>] [--tools <tools>]

Instructions

Default instructions for the MCP server is located in cmd/terraform-mcp-server/instructions.md, if those do not seem appropriate for your organization's Terraform practices or if the MCP server is producing inaccurate responses, please replace them with your own instructions and rebuild the container or binary. An example of such instruction is located in instructions/example-mcp-instructions.md

AGENTS.md essentially behaves as READMEs for coding agents: a dedicated, predictable place to provide the context and instructions to help AI coding agents work on your project. One AGENTS.md file works with different coding agents. An example of such instruction is located in instructions/example-AGENTS.md, in order to use it commit a file name AGENTS.md to the directory where your Terraform configurations reside.

Installation

Usage with Visual Studio Code

Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

More about using MCP server tools in VS Code's agent mode documentation.

json
{
  "mcp": {
    "servers": {
      "terraform": {
        "command": "docker",
        "args": [
          "run",
          "-i",
          "--rm",
          "-e", "TFE_TOKEN=${input:tfe_token}",
          "-e", "TFE_ADDRESS=${input:tfe_address}",
          "hashicorp/terraform-mcp-server:1.3.0"
        ]
      }
    },
    "inputs": [
      {
        "type": "promptString",
        "id": "tfe_token",
        "description": "Terraform API Token",
        "password": true
      },
      {
        "type": "promptString",
        "id": "tfe_address",
        "description": "Terraform Address",
        "password": false
      }
    ]
  }
}
json
{
  "mcp": {
    "servers": {
      "terraform": {
        "command": "docker",
        "args": [
          "run",
          "-i",
          "--rm",
          "hashicorp/terraform-mcp-server:0.2.3"
        ]
      }
    }
  }
}

Optionally, you can add a similar example (i.e. without the mcp key) to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

json
{
  "servers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "TFE_TOKEN=${input:tfe_token}",
        "-e", "TFE_ADDRESS=${input:tfe_address}",
        "hashicorp/terraform-mcp-server:1.3.0"
      ]
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "tfe_token",
      "description": "Terraform API Token",
      "password": true
    },
    {
      "type": "promptString",
      "id": "tfe_address",
      "description": "Terraform Address",
      "password": false
    }
  ]
}
json
{
  "servers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "hashicorp/terraform-mcp-server:0.2.3"
      ]
    }
  }
}

Usage with Cursor

Add this to your Cursor config (~/.cursor/mcp.json) or via Settings → Cursor Settings → MCP:

json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "TFE_ADDRESS=<<PASTE_TFE_ADDRESS_HERE>>",
        "-e", "TFE_TOKEN=<<PASTE_TFE_TOKEN_HERE>>",
        "hashicorp/terraform-mcp-server:1.3.0"
      ]
    }
  }
}
json
{
  "servers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "hashicorp/terraform-mcp-server:0.2.3"
      ]
    }
  }
}

Usage with Claude Desktop / Amazon Q Developer / Kiro CLI

More about using MCP server tools in Claude Desktop user documentation. Read more about using MCP server in Amazon Q Developer and Kiro CLI.

json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "TFE_ADDRESS=<<PASTE_TFE_ADDRESS_HERE>>",
        "-e", "TFE_TOKEN=<<PASTE_TFE_TOKEN_HERE>>",
        "hashicorp/terraform-mcp-server:1.3.0"
      ]
    }
  }
}
json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "hashicorp/terraform-mcp-server:0.2.3"
      ]
    }
  }
}

Usage with Claude Code

More about using and adding MCP server tools in Claude Code user documentation

  • Local (stdio) Transport
sh
claude mcp add terraform -s user -t stdio -- docker run -i --rm hashicorp/terraform-mcp-server
  • Remote (streamable-http) Transport
sh
# Run server (example)
docker run -p 8080:8080 --rm -e TRANSPORT_MODE=streamable-http -e TRANSPORT_HOST=0.0.0.0 hashicorp/terraform-mcp-server

# Add to Claude Code
claude mcp add --transport http terraform http://localhost:8080/mcp

Usage with Codex CLI

More about using and adding MCP server tools in Codex CLI user documentation.

Note: Add TFE_ADDRESS and TFE_TOKEN to the Docker commands for authenticated HCP Terraform or Terraform Enterprise tools.

  • Local (stdio) Transport
sh
codex mcp add terraform -- docker run -i --rm hashicorp/terraform-mcp-server
  • Remote (streamable-http) Transport
sh
# Run server (example)
docker run --rm -p 127.0.0.1:8080:8080 -e TRANSPORT_MODE=streamable-http -e TRANSPORT_HOST=0.0.0.0 hashicorp/terraform-mcp-server

# Add to Codex
codex mcp add terraform --url http://localhost:8080/mcp

Usage with Gemini extensions

For security, avoid hardcoding your credentials, create or update ~/.gemini/.env (where ~ is your home or project directory) for storing HCP Terraform or Terraform Enterprise credentials

# ~/.gemini/.env
TFE_ADDRESS=your_tfe_address_here
TFE_TOKEN=your_tfe_token_here

Install the extension & run Gemini

gemini extensions install https://github.com/hashicorp/terraform-mcp-server
gemini

Usage with Bob IDE / Shell

More about using and adding MCP servers tools in Bob IDE or Shell Using MCP in Bob.

json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "TFE_ADDRESS=<<PASTE_TFE_ADDRESS_HERE>>",
        "-e", "TFE_TOKEN=<<PASTE_TFE_TOKEN_HERE>>",
        "hashicorp/terraform-mcp-server:1.3.0"
      ],
      "disabled": false
    }
  }
}
json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "hashicorp/terraform-mcp-server:0.2.3"
      ],
      "disabled": false
    }
  }
}

Kubernetes (Helm)

A Helm chart for deploying the server on Kubernetes is available under helm/terraform-mcp-server.

Install from source

Use the latest release version:

console
go install github.com/hashicorp/terraform-mcp-server/cmd/terraform-mcp-server@latest

Use the main branch:

console
go install github.com/hashicorp/terraform-mcp-server/cmd/terraform-mcp-server@main
json
{
  "mcp": {
    "servers": {
      "terraform": {
        "type": "stdio",
        "command": "/path/to/terraform-mcp-server",
        "env": {
          "TFE_TOKEN": "<<TFE_TOKEN_HERE>>"
        },
      }
    }
  }
}
json
{
  "mcp": {
    "servers": {
      "terraform": {
        "type": "stdio",
        "command": "/path/to/terraform-mcp-server"
      }
    }
  }
}

Building the Docker Image locally

Before using the server, you need to build the Docker image locally:

  1. Clone the repository:
bash
git clone https://github.com/hashicorp/terraform-mcp-server.git
cd terraform-mcp-server
  1. Build the Docker image:
bash
make docker-build
  1. This will create a local Docker image that you can use in the following configuration.
bash
# Run in stdio mode
docker run -i --rm terraform-mcp-server:dev

# Run in streamable-http mode
docker run -p 8080:8080 --rm -e TRANSPORT_MODE=streamable-http -e TRANSPORT_HOST=0.0.0.0 terraform-mcp-server:dev

# Filter tools (optional)
docker run -i --rm terraform-mcp-server:dev --toolsets=registry,terraform
docker run -i --rm terraform-mcp-server:dev --tools=search_providers,get_provider_details

Note: When running in Docker, you should set TRANSPORT_HOST=0.0.0.0 to allow connections from outside the container.

  1. (Optional) Test connection in http mode
bash
# Test the connection
curl http://localhost:8080/health
  1. You can use it on your AI assistant as follow:
json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "terraform-mcp-server:dev"
      ]
    }
  }
}

Available Tools

Check out available tools here :link:

Available Resources

Check out available resources here :link:

Available Metrics

Two kinds of metrics are collected. First, standard HTTP server metrics are added by wrapping the HTTP mux with otelhttp.NewHandler(...). This emits:

  1. http.server.request.body.size
  2. http.server.response.body.size
  3. http.server.request.duration

Second, the MCP server records custom tool metrics around tool execution using MCP hooks (BeforeCallTool / AfterCallTool). These emit:

  1. mcp_tool_calls_total
  2. mcp_tool_errors_total
  3. mcp_tool_duration_seconds

Tool Filtering

Control which tools are available using --toolsets (groups) or --tools (individual):

bash
# Enable tool groups (default: registry)
terraform-mcp-server --toolsets=registry,terraform

# Enable specific tools only
terraform-mcp-server --tools=search_providers,get_provider_details,list_workspaces

Available toolsets: registry, registry-private, terraform, all, default. See pkg/toolsets/mapping.go for individual tool names. Cannot use both flags together.

Transport Support

The Terraform MCP Server supports multiple transport protocols:

1. Stdio Transport (Default)

Standard input/output communication using JSON-RPC messages. Ideal for local development and direct integration with MCP clients.

2. StreamableHTTP Transport

Modern HTTP-based transport supporting both direct HTTP requests and Server-Sent Events (SSE) streams. This is the recommended transport for remote/distributed setups.

Features:

  • Endpoint: http://{hostname}:8080/mcp
  • Health Check: http://{hostname}:8080/health
  • Environment Configuration: Set TRANSPORT_MODE=http or TRANSPORT_PORT=8080 to enable
  • Organization Allowlist: Set MCP_ORGANIZATION_ALLOWLIST or --organization-allowlist to a CSV list of allowed HCP Terraform organization names

Session Modes

The Terraform MCP Server supports two session modes when using the StreamableHTTP transport:

  • Stateful Mode (Default): Maintains session state between requests, enabling context-aware operations.
  • Stateless Mode: Each request is processed independently without maintaining session state, which can be useful for high-availability deployments or when using load balancers.

To enable stateless mode, set the environment variable:

bash
export MCP_SESSION_MODE=stateless

Token Passthrough for Centralized Deployments

When running the MCP server centrally (StreamableHTTP mode) for multiple users, each user can pass their own Terraform token via HTTP headers for RBAC enforcement. This allows a single server instance to serve multiple users with different permissions.

When MCP_ORGANIZATION_ALLOWLIST or --organization-allowlist is configured, the allowlist must be a CSV list of HCP Terraform organization names. The server requires Authorization: Bearer <token> and rejects requests unless that token can access at least one organization in the CSV allowlist. The bearer token takes precedence if the request also includes a TFE_TOKEN header, ensuring the token validated by the allowlist is the token used for Terraform API requests. Organization name matching is case-insensitive. If the configured CSV value parses to zero organization names, the server exits with a malformed organization allowlist error.

Client IP Forwarding

When running the MCP server centrally behind a proxy or load balancer, you can forward the originating client's IP to HCP Terraform / TFE via the X-Forwarded-For header. This is off by default and must be enabled with MCP_FORWARD_CLIENT_IP=true.

When enabled, the server sources the client IP according to MCP_REMOTE_IP_METHOD:

MethodBehavior
RemoteAddr (default)Uses only the address of the direct TCP connection. Ignores X-Forwarded-For and X-Real-IP.
X-Real-IPUses the X-Real-IP header if it is a valid IP, otherwise falls back to RemoteAddr.
X-Forwarded-ForUses the X-Forwarded-For chain, selecting the entry MCP_XFF_TRUSTED_HOPS positions from the right. Falls back to RemoteAddr if the value is missing or invalid.

Trust model

X-Forwarded-For and X-Real-IP are set by clients and intermediary proxies, so they can be spoofed unless a trusted proxy in front of the server overwrites them. For this reason the default is RemoteAddr, which trusts only the peer the server is directly connected to. Only enable X-Real-IP or X-Forwarded-For when the server sits behind a proxy you control that sets these headers.

Trusted hops

When using X-Forwarded-For, MCP_XFF_TRUSTED_HOPS is the number of proxies you operate between the server and the internet. Hops are counted from the right of the chain, since each proxy appends the address it received the request from and the rightmost entry is set by the proxy closest to the server. The server skips that many trusted entries and takes the next one to the left.

For example, with MCP_XFF_TRUSTED_HOPS=1 and a header of 200.1.2.3, 10.1.1.10, the server selects 200.1.2.3. With MCP_XFF_TRUSTED_HOPS=2 and 108.0.0.1, 200.1.2.3, 10.1.1.10, 192.168.0.1, it selects 200.1.2.3. If the hop count is greater than the number of entries, or the selected entry is not a valid IP, the server falls back to RemoteAddr.

Setting the hop count too low will trust a client-supplied value; setting it too high will trust an address further into your own infrastructure. Set it to the exact number of proxies you run.

Limitations

  • The server reads only the first X-Forwarded-For header on a request. It is valid for a request to carry multiple X-Forwarded-For headers, but Go's standard library returns only the first, and the server does not join them. If your proxy chain emits multiple headers, configure it to emit a single combined X-Forwarded-For header.
  • IPv4 and IPv6 addresses are both supported. Values that are not valid IPs are rejected and the server falls back to RemoteAddr.

Migrating from earlier versions

Earlier versions used the leftmost X-Forwarded-For value when the header was present, with no configuration. This was insecure, since the leftmost value is the most easily spoofed. The default is now RemoteAddr. If you run the server behind a proxy and rely on X-Forwarded-For being forwarded to HCP Terraform / TFE, set MCP_REMOTE_IP_METHOD=X-Forwarded-For and MCP_XFF_TRUSTED_HOPS to the number of proxies you operate.

Supported Headers

HeaderDescription
TFE_TOKENTerraform API token
Authorization: Bearer <token>Alternative method using standard Bearer auth
TFE_SKIP_TLS_VERIFYSkip TLS verification for the request

Example: curl

bash
# Using TFE_TOKEN header
curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -H "TFE_TOKEN: your-user-token" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize",...}'

# Using Authorization Bearer header
curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-user-token" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize",...}'

Security Considerations

  • TFE_ADDRESS cannot be set by clients. In streamable-http mode the Terraform address is sourced only from the server-side TFE_ADDRESS environment variable (or the default). Requests that attempt to set TFE_ADDRESS via HTTP header or query parameter are rejected with a 403. This prevents a client from redirecting requests, and the Authorization token, to a malicious server.
  • Hosted deployment identification: setting TF_MCP_SHARED_SECRET sends that value as the X-Tf-Mcp-Secret header on every HCP Terraform / TFE request, letting the backend identify requests from a known hosted deployment (e.g. to apply IP allowlists). It is a static secret sent in a header, so only use it over TLS and treat the value as a credential.
  • Never pass tokens in query parameters - the server will reject such requests with a 400 error.
  • Always use TLS (MCP_TLS_CERT_FILE/MCP_TLS_KEY_FILE) when deploying centrally to protect tokens in transit.
  • Configure MCP_ALLOWED_ORIGINS to restrict which clients can connect.

Centralized Deployment Example

bash
# Start server centrally (no token set server-side)
docker run -p 8080:8080 \
  -e TRANSPORT_MODE=streamable-http \
  -e TRANSPORT_HOST=0.0.0.0 \
  -e TFE_ADDRESS=https://tfe.company.com \
  -e MCP_TLS_CERT_FILE=/certs/server.pem \
  -e MCP_TLS_KEY_FILE=/certs/server-key.pem \
  -e MCP_ALLOWED_ORIGINS=https://ide.company.com \
  -e MCP_ORGANIZATION_ALLOWLIST=team-alpha,team-beta \
  -v /path/to/certs:/certs \
  hashicorp/terraform-mcp-server:1.3.0

Users then connect with their individual tokens passed via headers, enabling per-user RBAC enforcement.

Troubleshooting

Corporate Proxy / TLS Inspection (Zscaler, etc.)

If you're behind a corporate proxy that performs TLS inspection (like Zscaler Internet Access), you may see certificate errors:

tls: failed to verify certificate: x509: certificate signed by unknown authority

Solution: Mount your corporate CA certificate into the container:

bash
docker run -i --rm \
  -v /path/to/corporate-ca.pem:/etc/ssl/certs/corporate-ca.pem \
  -e SSL_CERT_FILE=/etc/ssl/certs/corporate-ca.pem \
  hashicorp/terraform-mcp-server:1.3.0

For MCP client configurations:

json
{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v", "/path/to/corporate-ca.pem:/etc/ssl/certs/corporate-ca.pem",
        "-e", "SSL_CERT_FILE=/etc/ssl/certs/corporate-ca.pem",
        "-e", "TFE_TOKEN=<>",
        "hashicorp/terraform-mcp-server:1.3.0"
      ]
    }
  }
}

Alternative: Run the binary directly

If Docker is not permitted in your environment, you can install and run the server binary directly, which will use your system's certificate store:

bash
go install github.com/hashicorp/terraform-mcp-server/cmd/terraform-mcp-server@latest
terraform-mcp-server stdio

Development

Prerequisites

  • Go (check go.mod file for specific version)
  • Docker (optional, for container builds)

Available Make Commands

CommandDescription
make buildBuild the binary
make testRun all tests
make test-e2eRun end-to-end tests
make docker-buildBuild Docker image
make run-httpRun HTTP server locally
make docker-run-httpRun HTTP server in Docker
make test-httpTest HTTP health endpoint
make cleanRemove build artifacts
make helpShow all available commands

Contributing

  1. Fork the repository
  2. Create your feature branch
  3. Make your changes
  4. Run tests
  5. Submit a pull request

License

This project is licensed under the terms of the MPL-2.0 open source license. Please refer to LICENSE file for the full terms.

Security

For security issues, please contact security@hashicorp.com or follow our security policy.

Support

For bug reports and feature requests, please open an issue on GitHub.

For general questions and discussions, open a GitHub Discussion.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "terraform": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "hashicorp/terraform-mcp-server"
      ]
    }
  }
}

Available Tools

  • search_providers

    Search for Terraform providers in the registry with filtering options

  • get_provider_documentation

    Retrieve comprehensive documentation for a specific Terraform provider

  • list_provider_versions

    List all available versions for a specific Terraform provider

  • search_modules

    Search for Terraform modules in the registry with filtering and sorting options

  • get_module_specification

    Retrieve detailed specifications and usage examples for a Terraform module

  • list_module_versions

    List all available versions for a specific Terraform module

  • get_module_readme

    Fetch the README documentation for a specific Terraform module

  • validate_hcl_syntax

    Validate Terraform HCL configuration syntax and return any errors

  • list_resource_types

    List all resource types available for a specific provider

  • get_resource_schema

    Retrieve the complete schema for a specific Terraform resource type

  • search_sentinel_policies

    Search for Sentinel policies in the Terraform Cloud policy registry

  • get_sentinel_policy

    Retrieve a specific Sentinel policy with its configuration and rules

  • validate_sentinel_policy

    Validate Sentinel policy syntax and test against sample data

  • list_data_sources

    List all data sources available for a specific Terraform provider

  • get_data_source_schema

    Retrieve the schema definition for a specific Terraform data source

  • format_hcl_configuration

    Format Terraform HCL configuration according to standard conventions

Use HashiCorp Terraform MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once HashiCorp Terraform is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add HashiCorp Terraform as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the HashiCorp Terraform MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "hashicorp-terraform": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the HashiCorp Terraform MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the HashiCorp Terraform MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use HashiCorp Terraform to help me with this task?
HashiCorp Terraform
Sure. I read it.
Here is what I found using HashiCorp Terraform.

Frequently asked questions

What is the HashiCorp Terraform MCP server used for?

HashiCorp Terraform is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use HashiCorp Terraform MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use HashiCorp Terraform with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use HashiCorp Terraform MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With HashiCorp Terraform connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect HashiCorp Terraform MCP to TypingMind?

HashiCorp Terraform runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does HashiCorp Terraform MCP provide in TypingMind?

HashiCorp Terraform exposes 16 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use HashiCorp Terraform MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If HashiCorp Terraform requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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