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MCP-Bridge
Cloud InfrastructureQuality Score: 40/99 (Fair)No Auth RequiredSpec v2018-11-29auto GenerationTransport: stdio

AmazonApiGatewayManagementApiMCP Configuration & Schema Registry

The AmazonApiGatewayManagementApi Model Context Protocol (MCP) configuration provides a validated, machine-readable JSON schema and executable bridge that connects state-of-the-art AI coding assistants — including Claude Desktop, Cursor IDE, Windsurf, Cline, and VS Code Copilot — directly to the AmazonApiGatewayManagementApi REST API. By leveraging the standardized open Model Context Protocol, AI agents can dynamically discover capabilities, validate input parameters against strict JSON Schemas, and execute live API operations without context switching or manual copy-pasting.

Quick Specs & Integration Summary

1. Functionality:Exposes 3 API endpoints as callable AI tools for AmazonApiGatewayManagementApi.
2. Authentication:Zero authentication required — ready for immediate execution.
3. Protocol Layer:Standard Model Context Protocol JSON-RPC 2.0 via stdio transport.
4. Quick Launch:npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json

Technical Architecture & Protocol Semantics

Under the Model Context Protocol specification, the AmazonApiGatewayManagementApi configuration functions as an isolated protocol adapter. When an AI agent initializes a session, the client establishes a bidirectional JSON-RPC 2.0 communication channel over standard input/output (stdio) or Server-Sent Events (SSE). During the initial handshake, the server publishes its tool manifest extracted from the AmazonApiGatewayManagementApi OpenAPI specification (version 2018-11-29).

The Amazon API Gateway Management API is a specialized, low-level service provided by Amazon Web Services (AWS) that grants developers direct, programmatic control over the runtime execution and lifecycle of their deployed APIs on Amazon API Gateway. Unlike the primary API Gateway service used for creating and defining APIs, this Management API is exclusively designed for post-deployment operational management, focusing on real-time interaction with active client connections. Its core capabilities are centered around managing WebSocket API connections, as evidenced by its endpoints which provide the ability to retrieve connection details, send messages to specific clients, and terminate connections. This API is indispensable for building and maintaining real-time, bidirectional applications such as live chat platforms, multiplayer gaming backends, collaborative editing tools, and real-time notification systems. It empowers backend services to initiate communication with connected clients, a crucial pattern for push-based architectures where the server needs to act without a prior client request. When the Amazon API Gateway Management API is exposed as a set of tools to an AI coding assistant via the Model Context Protocol (MCP), it unlocks a powerful paradigm of intelligent, runtime infrastructure management. An AI agent, such as Claude Desktop or Cursor, gains the ability to directly inspect and manipulate the state of live WebSocket connections, transforming it from a code generation aid into an active participant in application operations. This integration provides immense value for DevOps automation, real-time debugging, and dynamic system orchestration. For instance, an AI assistant could be instructed to query active connections for a specific user session to diagnose connectivity issues, broadcast a critical system update message to all connected clients during an emergency maintenance window, or gracefully terminate orphaned or misbehaving connections to free up resources, all through natural language commands translated into precise API calls. A developer can leverage this MCP server to instruct the AI agent to perform a variety of dynamic, context-aware tasks. The agent could be commanded to "list all current connections for the user 'analytics-service-01'" using the GET endpoint to monitor service health, or "send a targeted alert message to connection ID 'abc123'" via the POST endpoint to notify a specific client of a transaction completion. More complex workflows become possible, such as instructing the AI to "query all connections, identify those from the deprecated mobile app version, and send them a migration notice followed by termination." This facilitates automated, intelligent cleanup cycles. Furthermore, during development and testing, a developer could ask the AI to "simulate a user disconnect by deleting connection 'test123'" to validate reconnection logic, streamlining the quality assurance process for real-time applications. Critical attention must be paid to authentication and security when configuring this MCP server. The API itself employs "None" as its authentication method, which is a profound security consideration; this is because the endpoint is a private, customer-specific URL of the form `https://{api-id}.execute-api.{region}.amazonaws.com/{stage}`, not a public AWS service endpoint. Therefore, the paramount security practice is to ensure this endpoint URL is treated as a sensitive secret and never committed to public source code repositories or exposed in client-side applications. All access must be meticulously controlled at the network level, ideally within a private Virtual Private Cloud (VPC). When configuring the MCP server, developers should apply the principle of least privilege by generating and using API keys with the absolute minimum required permissions, which in this case are limited to the `execute-api:ManageConnections` action. Furthermore, all communication with this endpoint must be conducted over TLS to encrypt data in transit, and comprehensive logging and monitoring should be enabled to audit every management action performed by the AI agent or any other client. This architecture guarantees strict process boundary isolation: all sensitive authorization headers and secret tokens remain sandboxed inside the client runtime, never leaking into language model context windows or external logging endpoints.

Authentication TypePublic (No Auth)Injected via local client environment
Tools & Routes Mapped3 OperationsConforms to JSON-RPC 2.0 specs
Specification OriginOpenAPI v2018-11-29auto schema validation
Documentation & Schema Quality Index
40
★ Grade C - Baseline Coverage
Automated Audit Checklist
✓Automated schema extraction & validation (+12 pts)
✓Core tool mapping (3 endpoints defined) (+14 pts)
✓Zero-configuration public API instant execution (+20 pts)
✓Full JSON-RPC 2.0 Model Context Protocol specification conformity (+15 pts)
✓Upstream technical documentation verification (+12 pts)

Hosted Remote Configuration URL

MCP Configuration File

Provide this hosted URL in any client that supports remote MCP schema auto-loading.

https://mcpbridge.org/config/amazonaws-com-apigatewaymanagementapi.json
Download JSON SchemaClaude Desktop Guide →

2. AI Assistant Use Cases & Practical Workflows

Tailored for Cloud Infrastructure

Real-world execution scenarios demonstrating how LLM agents (Claude 3.7, GPT-4o, Cursor Agent) invoke AmazonApiGatewayManagementApi tools to automate developer workflows.

1. CI/CD Build Failure & Telemetry Diagnostics

CI/CD Remediation

Instantly diagnose failing CI/CD builds or deployment pipelines by streaming build logs, isolating failure root causes, and drafting targeted code fixes.

Example Natural Language Prompt:

"Fetch recent pipeline run logs from AmazonApiGatewayManagementApi. Isolate the failed step, summarize the exact compiler or test failure error, and propose a pull request fix in Cursor."

Mapped: /@connections/{connectionId}

2. Cloud Resource Auditing & Cost Optimization

Cloud FinOps

Scan active compute clusters, storage buckets, and networking configurations to identify unattached volumes or idle oversized instances.

Example Natural Language Prompt:

"Query active cloud infrastructure resources in AmazonApiGatewayManagementApi. Identify unattached storage volumes, idle compute instances, and summarize estimated monthly cost savings."

Mapped: /@connections/{connectionId}

3. Zero-Downtime Rollout & Canary Health Verification

Deployment Ops

Orchestrate progressive deployments, monitor error rate thresholds on newly deployed pods, and execute automated rollbacks if error budgets breach.

Example Natural Language Prompt:

"Check the active deployment rollout status in AmazonApiGatewayManagementApi. Monitor canary error rate percentages for 5 minutes and report whether the deployment is safe to promote to 100% traffic."

Autonomous Agent Loop

4. Infrastructure as Code (IaC) Drift Detection

IaC Governance

Compare live deployed resource state against Terraform or CloudFormation definitions to spot unauthorized manual changes.

Example Natural Language Prompt:

"Scan live configurations via AmazonApiGatewayManagementApi and compare against our repository IaC definitions. Highlight any configuration drift in security groups or network routes."

Autonomous Agent Loop

End-to-End Multi-Step Agent Execution Lifecycle

When an engineer submits a task to Claude Desktop or Cursor, the LLM executes an autonomous 4-phase Model Context Protocol loop:

Phase 1

Schema Introspection

Handshake lists all 3 tools and builds argument validators.

Phase 2

Argument Synthesis

Model extracts parameters from prompt and validates types against OpenAPI rules.

Phase 3

Stdio Execution

Bridge invokes live API with injected local credentials and captures raw HTTP response.

Phase 4

Output Remediation

LLM parses JSON results, handles status codes, and presents synthesized answers.

3. Multi-Client Installation Matrix & Setup Guides

Select your AI assistant below to view exact configuration file paths, JSON installation snippets, and launch commands.

Claude Desktop

claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "amazonaws-com-apigatewaymanagementapi": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json"
      ],
      "env": {
        "AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY": "your_amazonapigatewaymanagementapi_api_key"
      }
    }
  }
}
Deep link

Cursor IDE

.cursor/mcp.json

Open Cursor Settings → Features → MCP Servers, or create .cursor/mcp.json in your project root.

{
  "mcpServers": {
    "amazonaws-com-apigatewaymanagementapi": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json"
      ],
      "env": {
        "AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY": "your_amazonapigatewaymanagementapi_api_key"
      }
    }
  }
}

Saves as .cursor/mcp.json in the download. Move it to your project root.

Deep link install →

VS Code / Cline Extension

cline_mcp_settings.json

Paste into your Cline extension MCP configuration or Roo Code host settings.

{
  "mcpServers": {
    "amazonaws-com-apigatewaymanagementapi": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json"
      ],
      "env": {
        "AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY": "your_amazonapigatewaymanagementapi_api_key"
      }
    }
  }
}

Zed Editor & Docker CLI

Zed / Docker

Docker container execution command:

docker run -i --rm -e AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY="YOUR_SECRET_VALUE" node:20-alpine npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json

Zed settings context servers JSON:

{
  "context_servers": {
    "amazonaws-com-apigatewaymanagementapi": {
      "command": {
        "path": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-openapi",
          "https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json"
        ],
        "env": {
          "AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY": "your_amazonapigatewaymanagementapi_api_key"
        }
      }
    }
  }
}

Programmatic SDK Integration (TypeScript / Python)

Initialize the AmazonApiGatewayManagementApi MCP client directly in your backend codebase.

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

// Initialize AmazonApiGatewayManagementApi MCP client transport over stdio
const transport = new StdioClientTransport({
  command: "npx",
  args: ["-y","@modelcontextprotocol/server-openapi","https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json"],
  env: { AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY: process.env.AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY || "YOUR_SECRET_KEY" }
});

const client = new Client(
  { name: "amazonaws-com-apigatewaymanagementapi-client", version: "1.0.0" },
  { capabilities: { tools: {}, resources: {}, prompts: {} } }
);

async function connectAndRun() {
  await client.connect(transport);
  const tools = await client.listTools();
  console.log("Connected to AmazonApiGatewayManagementApi MCP Server.");
  console.log("Discovered 3 mapped tools:", tools);
}

connectAndRun().catch(console.error);

Raw Stdio Schema Definition

schema.json

For standalone CLI wrappers, background daemon daemons, or custom script integrations:

{
  "mcpServers": {
    "amazonaws-com-apigatewaymanagementapi": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json"
      ],
      "env": {
        "AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEY": "your_amazonapigatewaymanagementapi_api_key"
      }
    }
  }
}

4. Security, Authentication & Credential Management

Safely configure authentication tokens, isolate execution environments, and implement enterprise security best practices.

Required Environment Keys Reference

Variable NameRequiredTypeDefaultPurpose & Guidance
AMAZONAPIGATEWAYMANAGEMENTAPI_API_KEYREQUIREDSecret Key / TokenNone (Set in env)your_amazonapigatewaymanagementapi_api_key

Zero-Downtime Token Rotation Protocol

  1. Generate Secondary Key: Create a new secret API token with identical scopes in your AmazonApiGatewayManagementApi developer portal.
  2. Update Client Configuration: Insert the new token inside the env block of your MCP client JSON config.
  3. Validate Connection: Issue a test query in Claude or Cursor to ensure handshake and tool calls succeed.
  4. Revoke Stale Token: Decommission the legacy key on the vendor portal to prevent unauthorized access.

Least-Privilege & Sandboxing Rules

  • Read-Only Token Scoping: Whenever your workflow only requires querying data, provision read-only credentials to prevent accidental mutations.
  • Local Process Isolation: Stdio transports run in isolated local subprocesses; secret credentials are never sent across the internet to MCP Bridge servers.
  • Prompt Injection Defense: AI model responses are sandboxed; verify generated destructive arguments before confirming execution in agent mode.

Enterprise Security Checklist (Mandatory Practices)

  • Never commit claude_desktop_config.json or .cursor/mcp.json containing raw secrets into public GitHub repositories.
  • Add .cursor/mcp.json and .env.local to your project's .gitignore file.
  • Always enforce TLS/HTTPS encryption on outbound network requests initiated by the server process.

5. Tool Parameter Schemas & Natural Language Execution

Mapped OpenAPI operations converted into discrete Model Context Protocol tools with strict JSON-RPC payload validators.

3 Total Tools Mapped
GET/@connections/{connectionId}
tools/call: amazonaws-com-apigatewaymanagementapi_get_connections__connectionId

GetConnection

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "amazonaws-com-apigatewaymanagementapi_get_connections__connectionId",
    "arguments": {}
  }
}
Natural Language Prompt

"Use AmazonApiGatewayManagementApi to execute GetConnection and output the formatted result."

POST/@connections/{connectionId}
tools/call: amazonaws-com-apigatewaymanagementapi_post_connections__connectionId

PostToConnection

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "amazonaws-com-apigatewaymanagementapi_post_connections__connectionId",
    "arguments": {}
  }
}
Natural Language Prompt

"Use AmazonApiGatewayManagementApi to execute PostToConnection and output the formatted result."

DELETE/@connections/{connectionId}
tools/call: amazonaws-com-apigatewaymanagementapi_delete_connections__connectionId

DeleteConnection

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "tools/call",
  "params": {
    "name": "amazonaws-com-apigatewaymanagementapi_delete_connections__connectionId",
    "arguments": {}
  }
}
Natural Language Prompt

"Use AmazonApiGatewayManagementApi to execute DeleteConnection and output the formatted result."

6. Interactive Troubleshooting & FAQ Accordion

Diagnose and resolve common JSON-RPC protocol error codes, connection disconnects, and schema refresh issues.

A 401 Unauthorized response indicates that the upstream AmazonApiGatewayManagementApi API rejected the authentication credential supplied in your MCP client's environment configuration. To resolve this: (1) Verify that your secret token is defined inside the "env" block of claude_desktop_config.json or .cursor/mcp.json rather than hardcoded in the command string. (2) Check whether AmazonApiGatewayManagementApi requires a prefix such as "Bearer <token>" in the authorization header. (3) Confirm that your API key has not expired and has been granted sufficient least-privilege scopes on the AmazonApiGatewayManagementApi developer dashboard.

If your MCP client fails to initialize tools for AmazonApiGatewayManagementApi: (1) Test the bridge launcher command ("npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json") directly inside your terminal or shell to inspect stdout/stderr diagnostic traces. (2) Verify network connectivity to the schema source (https://api.apis.guru/v2/specs/amazonaws.com/apigatewaymanagementapi/2018-11-29/openapi.json). (3) Ensure Node.js (v18+) is installed and accessible in your system PATH. (4) For authenticated APIs, confirm credentials are configured in your client's "env" mapping rather than command arguments.

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https://mcpbridge.org/config/supabase.json

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https://mcpbridge.org/config/cloudflare.json

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DigitalOcean API

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The DigitalOcean API is a comprehensive, RESTful interface provided by DigitalOcean, a leading cloud infrastructure provider focused on simplifying cloud computing for developers, startups, and enterprises. It serves as the programmatic backbone for managing the entire DigitalOcean ecosystem, enabling users to provision, configure, and control cloud resources such as Droplets (virtual private servers), Kubernetes clusters, managed databases, networks, storage volumes, and application platforms. Core capabilities include full lifecycle management of these resources, from creation and scaling to monitoring and deletion, mirroring the functionality available in the DigitalOcean control panel. Its primary use cases range from automating infrastructure setup for CI/CD pipelines and enabling infrastructure-as-code practices to supporting dynamic application scaling and resource optimization for SaaS products, e-commerce sites, and development environments. The API is designed for both developers seeking to automate their cloud operations and businesses that require programmable, scalable cloud infrastructure without the complexity of larger hyperscale providers. When exposed as tools via the Model Context Protocol (MCP) to an AI coding assistant, the DigitalOcean API transforms from a traditional developer tool into a dynamic, context-aware resource for intelligent infrastructure automation. The MCP server acts as a bridge, allowing the AI model to understand and execute API calls based on natural language instructions and the current project context. This integration provides immense value by enabling the AI to perform real-time cloud management tasks directly within the development workflow. For instance, the AI can instantly query account details to verify resources, list and manage SSH keys for secure access, or retrieve and monitor the status of infrastructure actions. This contextual access means the AI can make informed suggestions or take automated actions—like recommending a cost-optimized Droplet size based on current usage patterns or verifying that a new SSH key has been correctly added before proceeding with a deployment script—thereby reducing context-switching and accelerating development cycles. Practical workflow examples demonstrate the power of this MCP integration. A developer could instruct the AI agent with commands like, "Query our account for all active SSH keys and ensure the one named 'ci-bot' is present; if not, create it using this public key," automating a common security and setup step. Another example involves asking the AI to "Check the status of our last ten infrastructure actions to see if any are stuck in a 'pending' state," which would leverage the actions endpoints to provide an immediate operational health check. More complex automations are possible, such as "Based on the current Droplet inventory from the API, generate a Terraform configuration file that replicates this setup," or "Scan our Kubernetes 1-Click apps and suggest one for deploying a new microservice based on the project requirements." These interactions turn the AI into a proactive DevOps partner capable of auditing, reporting, and modifying cloud infrastructure through simple, conversational directives. Critical to the secure operation of this MCP server is rigorous attention to authentication and access control, despite any initial configuration notes indicating "None" for simplicity. In any real-world deployment, authentication via a DigitalOcean Personal Access Token is non-negotiable. This token should be treated as a high-privilege secret. Developers must adhere to the principle of least privilege by creating tokens with the minimum scopes required for the specific tasks—such as read-only access for monitoring or write access only for specific resource types. Best practices include storing tokens in secure environment variables or a secrets manager, never hardcoding them, and ensuring the MCP server configuration does not expose them in logs or client-side code. Furthermore, regular token rotation and monitoring of API activity through DigitalOcean's audit logs are essential to maintain a secure posture when integrating cloud management capabilities directly into AI-assisted development environments.

https://mcpbridge.org/config/digitalocean-com.json