DigitalOcean API MCP Configuration
The DigitalOcean API MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the DigitalOcean API API via the Model Context Protocol. This configuration maps 10 API endpoints as callable tools, including List 1-Click Applications, Install Kubernetes 1-Click Applications, Get User Information, and more. No authentication credentials are needed — it works out of the box. The configuration is auto-generated from the DigitalOcean API OpenAPI specification (v2.0) and has a quality score of 34/99 (fair documentation coverage). Use the hosted URL below to auto-load this schema into any compatible MCP client.
Quick Specs Reference
Hosted Config URL
Use this hosted URL in any client that supports remote MCP schema auto-loading.
https://mcpbridge.org/config/digitalocean-com.jsonOne-Click Client Setup
Copy the configurations below to wire your local coding assistant directly.
Claude Desktop
claude_desktop_config.json{
"mcpServers": {
"digitalocean-com": {
"command": "npx",
"args": [
"-y",
"@mcp/digitalocean-com"
],
"env": {
"DIGITALOCEAN_API_API_KEY": "your_digitalocean_api_api_key"
}
}
}
}Cursor & VS Code
MCP Server URL Setup{
"mcpServers": {
"digitalocean-com": {
"url": "https://mcpbridge.org/config/digitalocean-com.json"
}
}
}Raw Configuration JSON
For local command line wrappers or dynamic shell bindings.
{
"mcpServers": {
"digitalocean-com": {
"command": "npx",
"args": ["-y","@mcp/digitalocean-com"],
"env": {
"DIGITALOCEAN_API_API_KEY": "your_digitalocean_api_api_key"
}
}
}
}Required Environment Keys
Substitute these secrets inside your configuration directory environment definitions.
DIGITALOCEAN_API_API_KEYyour_digitalocean_api_api_key with your secret key credentialMapped Web APIs & Tools
The following routes will be exposed directly as protocol tools for the LLM.
/v2/1-clicksList 1-Click Applications
/v2/1-clicks/kubernetesInstall Kubernetes 1-Click Applications
/v2/accountGet User Information
/v2/account/keysList All SSH Keys
/v2/account/keysCreate a New SSH Key
/v2/account/keys/{ssh_key_identifier}Retrieve an Existing SSH Key
/v2/account/keys/{ssh_key_identifier}Update an SSH Key's Name
/v2/account/keys/{ssh_key_identifier}Delete an SSH Key
/v2/actionsList All Actions
/v2/actions/{action_id}Retrieve an Existing Action
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The AWS Identity and Access Management Access Analyzer API provides a powerful, policy-as-code service that automatically identifies resources accessible from outside your AWS account or organization. At its core, the service continuously evaluates resource-based policies—such as Amazon S3 bucket policies, AWS Identity and Access Management (IAM) roles, Amazon KMS key policies, and AWS Lambda function policies—using logic-based reasoning to determine which resources grant access to unknown external principals. Its primary use case is for security and compliance teams within enterprises to proactively detect unintended data exposure, enforce least privilege principles, and audit cross-account and cross-service access. The API endpoints allow programmatic control to create, configure, and query analyzers, manage archive rules for storing findings, and generate custom policy documents, making it a foundational tool for automating cloud security posture management at scale. When exposed as tools through the Model Context Protocol (MCP) to an AI coding assistant like Claude Desktop or Cursor, the Access Analyzer API transforms from a cloud management tool into a dynamic, conversational security consultant for developers. The AI agent gains the ability to directly interact with the analyzer's intelligence layer, enabling a workflow where a developer can ask natural language questions like, "Analyze my S3 bucket named 'customer-data' for any public access risks," and the AI can orchestrate the appropriate API calls to fetch and interpret the latest findings. This integration significantly lowers the barrier to entry for complex security analysis, allowing developers without deep IAM expertise to get actionable insights within their IDE. The AI can also assist in policy remediation by using the policy generation endpoints to draft least-privilege policies based on the access patterns identified by the analyzer. Practical workflows enabled by this MCP server include continuous security auditing and automated policy refinement. A developer can instruct the AI agent to perform tasks such as: "Query all active analyzers and summarize the most critical high-severity findings from the last 24 hours," or "Create a new analyzer for my organization's member accounts and configure an archive rule to store resolved findings in this S3 bucket." The AI can further automate lifecycle management by saying, "Review the findings for IAM roles created by CloudFormation in the dev environment and use the policy generation tool to propose a tightened policy that only allows the necessary API actions based on observed usage." This creates a powerful feedback loop where the AI acts as an intermediary between the developer's intent and the service's analytical capabilities, enabling proactive security hardening and drift detection without manual console navigation. Critical security practices must be paramount when configuring this server. Although the API itself may use various authentication mechanisms, granting an AI agent access to these powerful tools requires strict adherence to the principle of least privilege. The IAM role or user credentials provided to the MCP server should have a minimal, scoped-down permission set, ideally restricted to read-only access to specific analyzer resources and the necessary findings reporting actions. Developers should avoid providing broad administrative permissions. It is essential to use managed policies or create custom policies that only allow actions like `accessanalyzer:GetAnalyzer`, `accessanalyzer:ListFindings`, and `accessanalyzer:ListAnalyzers`. Furthermore, sensitive analysis should be confined to designated accounts or regions, and all AI-agent-driven actions should be logged and monitored through AWS CloudTrail to maintain a clear audit trail of automated interactions with this critical security service.
https://mcpbridge.org/config/amazonaws-com-accessanalyzer.json