Provides tools for interacting with Terraform Cloud API, enabling management and monitoring of workspaces, runs, and their statuses across organizations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Terraform Cloud MCP Server what's the current run status for the production-infrastructure workspace?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol server providing Terraform Cloud API integration as tools for AI assistants.
This MCP server exposes four main tools for interacting with Terraform Cloud:
get_run_status: Get current run status and recent runs for a workspace
list_workspaces: List all workspaces in an organization
get_workspace_details: Get detailed information about a specific workspace
get_run_details: Get detailed information about a specific run by its ID
Related MCP server: Terraform MCP Server
npm install
npm run buildThe server reads your Terraform Cloud token from ~/.terraform.d/credentials.tfrc.json. Make sure this file exists with the following format:
{
"credentials": {
"app.terraform.io": {
"token": "your-terraform-cloud-token"
}
}
}Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"terraform-cloud": {
"command": "node",
"args": ["/absolute/path/to/tf-cloud-mcp-server/build/index.js"]
}
}
}Test your server with the MCP Inspector:
npx @modelcontextprotocol/inspector node build/index.jsCreate a .vscode/mcp.json file in your project:
{
"servers": {
"terraform-cloud": {
"type": "stdio",
"command": "node",
"args": ["/absolute/path/to/tf-cloud-mcp-server/build/index.js"]
}
}
}Get the current run status for a Terraform Cloud workspace.
Parameters:
workspaceName (string, required): Name of the workspace
organization (string, optional): Organization name (default: "urbanmedia")
Example:
Get the run status for workspace "capivara-dpa-importer"List all workspaces in a Terraform Cloud organization.
Parameters:
organization (string, optional): Organization name (default: "urbanmedia")
Example:
List all workspaces in urbanmedia organizationGet detailed information about a specific workspace.
Parameters:
workspaceName (string, required): Name of the workspace
organization (string, optional): Organization name (default: "urbanmedia")
Example:
Get details for workspace "capivara-dpa-importer"Get detailed information about a specific Terraform Cloud run by its ID.
Parameters:
runId (string, required): Run ID (e.g., "run-abc123")
Example:
Get details for run "run-abc123"tf-cloud-mcp-server/
├── src/
│ └── index.ts # Main server implementation
├── build/ # Compiled JavaScript (generated)
├── package.json
├── tsconfig.json
└── README.mdnpm run dev # Watch mode for TypeScript compilationnpm run buildMIT
Get detailed information about a specific Terraform Cloud run by its ID
| Name | Required | Description | Default |
|---|---|---|---|
| runId | Yes | Run ID (e.g., run-abc123) |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| source | Yes | |
| status | Yes | |
| message | Yes | |
| createdAt | Yes | |
| workspace | Yes | |
| hasChanges | Yes | |
| planStatus | No | |
| applyStatus | No | |
| isConfirmable | Yes | |
| configurationVersion | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't specify authentication requirements, rate limits, error conditions, or what 'detailed information' entails (e.g., includes logs, configuration, or status). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly, and every part of the sentence contributes essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete for its purpose. However, it lacks context on behavioral aspects like authentication or error handling, which are important for a tool interacting with a cloud service like Terraform Cloud, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'runId' fully documented in the schema as 'Run ID (e.g., run-abc123)'. The description adds no additional parameter details beyond what the schema provides, such as format constraints or examples, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get detailed information') and resource ('about a specific Terraform Cloud run by its ID'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_run_status' or 'get_workspace_details', which might offer overlapping or related information about runs or workspaces.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_run_status' (which might provide less detailed status info) or 'get_workspace_details' (which might include run info as part of workspace data), leaving the agent to infer usage context based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current run status for a Terraform Cloud workspace
| Name | Required | Description | Default |
|---|---|---|---|
| workspaceName | Yes | Workspace name | |
| organization | No | Organization name | urbanmedia |
| Name | Required | Description |
|---|---|---|
| recentRuns | Yes | |
| workspaceId | Yes | |
| currentRunId | No | |
| workspaceName | Yes | |
| currentRunStatus | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a read operation ('Get'), implying it's non-destructive, but doesn't mention any behavioral traits like authentication requirements, rate limits, error handling, or what 'status' entails (e.g., pending, running, failed). This leaves significant gaps for an agent to understand how to interact with it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a read operation with 2 parameters) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks context about behavioral aspects and usage guidelines, which are important for an agent to operate correctly, especially with sibling tools available.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters ('workspaceName' and 'organization' with a default). The description adds no additional meaning beyond this, such as explaining parameter relationships or usage context. This meets the baseline of 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('current run status for a Terraform Cloud workspace'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_run_details' or 'get_workspace_details', which likely provide related but different information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_run_details' or 'get_workspace_details'. It lacks context about prerequisites, such as needing an existing run or workspace, or any exclusions for when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a Terraform Cloud workspace
| Name | Required | Description | Default |
|---|---|---|---|
| workspaceName | Yes | Workspace name | |
| organization | No | Organization name | urbanmedia |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| name | Yes | |
| locked | Yes | |
| vcsRepo | No | |
| autoApply | Yes | |
| executionMode | Yes | |
| terraformVersion | Yes | |
| workingDirectory | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), implying it's non-destructive, but doesn't mention any behavioral traits like authentication needs, rate limits, error handling, or what 'detailed information' entails beyond the output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with no wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, 100% schema coverage, and presence of an output schema, the description is minimally adequate. However, it lacks context about when to use it versus siblings and behavioral details, which are gaps despite the structured data support.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents both parameters ('workspaceName' and 'organization') adequately. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or constraints, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'detailed information about a Terraform Cloud workspace', making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'list_workspaces' or 'get_run_details', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'list_workspaces' for a broader overview or 'get_run_details' for run-specific information. The description lacks any context about prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all workspaces in a Terraform Cloud organization
| Name | Required | Description | Default |
|---|---|---|---|
| organization | No | Organization name | urbanmedia |
| Name | Required | Description |
|---|---|---|
| workspaces | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a listing operation but doesn't mention whether it's paginated, what permissions are required, rate limits, or what the output format looks like. For a read operation with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which handles return values) and 100% schema coverage for its single parameter, the description is minimally adequate. However, as a read operation with no annotations, it should ideally provide more behavioral context about permissions, pagination, or limitations to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents the single parameter 'organization' with its type, default value, and description. The tool description doesn't add any additional meaning or context about this parameter beyond what's in the schema, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all workspaces') and resource ('in a Terraform Cloud organization'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_workspace_details' which suggests this is a listing operation versus a detailed retrieval, but this distinction isn't explicitly stated in the description itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_workspace_details' or other sibling tools. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get_run_detailsget_run_statusget_workspace_detailslist_workspacesThe tools are mostly distinct, with clear separation between workspace operations (list_workspaces, get_workspace_details) and run operations (get_run_details, get_run_status). However, get_run_details and get_run_status could potentially be confused since both relate to run information, though their descriptions differentiate them as 'detailed information' versus 'current status'.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., get_run_details, list_workspaces). The verbs 'get' and 'list' are used appropriately and predictably throughout the set.
With only 4 tools, the server feels thin for managing Terraform Cloud resources. While it covers basic read operations, the scope suggests more comprehensive management (e.g., create/update/delete operations) would be expected, making the count borderline for the domain.
The toolset is severely incomplete for Terraform Cloud management. It only provides read operations (get and list) with no ability to create, update, or delete workspaces, runs, or other resources. This will cause significant agent failures when attempting full lifecycle management.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
Plan Salesforce deploys, open pull requests and trigger pipelines from your AI client.
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
Give your AI agents the tools to build, manage, and run automation workflows.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/matchs/tf-cloud-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
