de en es ja ko ru zh

Amazon VPC Lattice MCP Server

Name: Amazon VPC Lattice MCP Server
Author: rlymbur

by rlymbur

Overview Schema Source Related Servers Score Discussions Admin

TypeScript

Hybrid

Amazon VPC Lattice MCP 서버

AWS VPC Lattice 리소스와 관련 문서에 액세스하고 관리하기 위한 도구를 제공하는 소스 목록을 위한 MCP(모델 컨텍스트 프로토콜) 서버입니다.

특징

서버는 5가지 주요 도구를 제공합니다.

list_sources : 사용 가능한 모든 소스를 URL 및 샘플 프롬프트와 함께 나열합니다.
get_source_prompts : 특정 소스에 대한 샘플 프롬프트를 가져옵니다.
list_amazon_vpc_lattice_prompts : 사용 가능한 모든 프롬프트 템플릿을 나열합니다.
get_amazon_vpc_lattice_prompts : 특정 프롬프트 템플릿의 세부 정보를 가져옵니다.
vpc_lattice_cli : VPC Lattice 리소스를 관리하기 위한 AWS CLI VPC Lattice 명령 실행

Related MCP server: Log Analyzer with MCP

설치

이 프로젝트는 TypeScript로 작성되었으며 ES 모듈을 사용합니다.

저장소를 복제합니다.

지엑스피1

종속성 설치:

npm install

서버를 빌드하세요:

npm run build

빌드 스크립트는 TypeScript 코드를 컴파일하고 적절한 실행 권한을 설정합니다.

구성

MCP 설정 파일( ~/Library/Application Support/Code/User/globalStorage/asbx.amzn-cline/settings/cline_mcp_settings.json 에 위치)에 서버를 추가합니다.

{
  "mcpServers": {
    "amazon-vpc-lattice": {
      "command": "node",
      "args": ["/path/to/amazon-vpc-lattice-mcp-server/build/index.js"],
      "disabled": false,
      "autoApprove": [],
      "env": {}
    }
  }
}

용법

구성이 완료되면 대화에서 MCP 도구를 사용할 수 있습니다. 사용 가능한 프롬프트는 도구처럼 자동으로 검색되지 않으므로 list_amazon_vpc_lattice_prompts 사용하여 검색해야 합니다.

소스 목록

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "list_sources",
  arguments: {}
})

소스 프롬프트 가져오기

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "get_source_prompts",
  arguments: {
    source_name: "AWS Documentation"
  }
})

Amazon VPC Lattice 프롬프트 목록

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "list_amazon_vpc_lattice_prompts",
  arguments: {}
})

Amazon VPC Lattice 프롬프트 세부 정보 가져오기

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "get_amazon_vpc_lattice_prompts",
  arguments: {
    prompt_name: "setup_eks_controller"
  }
})

VPC 격자 CLI

vpc_lattice_cli 도구는 AWS CLI를 통해 AWS VPC Lattice 작업에 대한 프로그래밍 인터페이스를 제공합니다.

특징

모든 주요 VPC Lattice CLI 작업을 지원합니다.
JavaScript 객체로 명령 인수를 허용합니다.
camelCase 매개변수를 CLI 스타일 케밥 케이스로 자동 변환합니다.
부울 플래그, 배열 및 복소수 값을 처리합니다.
AWS 프로필 및 지역 구성을 지원합니다.
구문 분석된 JSON 응답을 반환합니다.

사용 가능한 명령

서비스 네트워크: create-service-network, delete-service-network, get-service-network, list-service-networks, update-service-network
서비스: create-service, delete-service, get-service, list-services, update-service
리스너: create-listener, delete-listener, get-listener, list-listeners, update-listener
규칙: create-rule, delete-rule, get-rule, list-rule, update-rule
대상 그룹: create-target-group, delete-target-group, get-target-group, list-target-groups, update-target-group
타겟 관리: register-targets, deregister-targets, list-targets
리소스 태그: list-tags-for-resource, tag-resource, untag-resource

예시

서비스 네트워크 나열:

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "vpc_lattice_cli",
  arguments: {
    command: "list-service-networks",
    region: "us-west-2"
  }
})

서비스 네트워크 생성:

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "vpc_lattice_cli",
  arguments: {
    command: "create-service-network",
    args: {
      name: "my-network",
      authType: "NONE"
    }
  }
})

태그를 사용하여 서비스를 만듭니다.

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "vpc_lattice_cli",
  arguments: {
    command: "create-service",
    args: {
      name: "my-service",
      serviceNetworkIdentifier: "sn-12345",
      tags: [
        { key: "Environment", value: "Production" }
      ]
    }
  }
})

타겟 그룹을 만드세요:

use_mcp_tool({
  server_name: "amazon-vpc-lattice",
  tool_name: "vpc_lattice_cli",
  arguments: {
    command: "create-target-group",
    args: {
      name: "my-target-group",
      type: "INSTANCE",
      config: {
        port: 80,
        protocol: "HTTP",
        healthCheck: {
          enabled: true,
          protocol: "HTTP",
          path: "/health"
        }
      }
    }
  }
})

사용 가능한 소스

서버에는 다음 소스가 포함되어 있습니다.

AWS 설명서(docs.aws.amazon.com)
- 주요 기능 쿼리
- 구성 지침
- 모범 사례
VPC Lattice용 AWS Gateway API 컨트롤러(aws/aws-application-networking-k8s)
- 기능 지원 문의
- 이슈 추적
쿠버네티스 게이트웨이 API(gateway-api.sigs.k8s.io)
- 오류 해결
- 모범 사례 지침

개발

프로젝트 구조

이 프로젝트는 다음과 같이 구성됩니다.

src/index.ts : 메인 서버 설정 및 초기화
src/tools.ts : 도구 정의 및 핸들러
src/data/ : 데이터 파일
- prompts.ts : 프롬프트 템플릿 및 매개변수
- sources.ts : 소스 정의 및 프롬프트
package.json : 프로젝트 구성 및 종속성
tsconfig.json : TypeScript 구성
.gitignore : Git 무시 규칙
build/ : 컴파일된 JavaScript 출력

새로운 소스 추가

새로운 소스를 추가하려면 src/data/sources.ts 에 있는 sources 배열을 수정하세요.

export const sources = [
  {
    name: 'Your Source',
    url: 'https://your-source-url.com',
    prompts: [
      'Sample prompt 1 {placeholder}',
      'Sample prompt 2 {placeholder}'
    ]
  }
  // ... existing sources
];

새로운 프롬프트 추가

새로운 프롬프트 템플릿을 추가하려면 src/data/prompts.ts 에 있는 prompts 배열을 수정하세요.

export const prompts = [
  {
    name: 'Your Prompt Template',
    description: 'Description of what the prompt does',
    template: 'Your prompt template with {parameter} placeholders',
    parameters: ['parameter']
  }
  // ... existing prompts
];

스크립트

npm run build : 서버를 빌드하고 실행 권한을 설정합니다.
npm run watch : 개발을 위한 감시 모드
npm test : 테스트 실행(현재 구현되지 않음)

Available Tools

5 tools

get_amazon_vpc_lattice_promptsC

Get details of a specific prompt template

ParametersJSON Schema

Name	Required	Description	Default
`prompt_name`	Yes	Name of the prompt template to get

TDQS

C2.9/5.0

Behavior2/5

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. The description only states what the tool does ('Get details') without mentioning any behavioral traits such as permissions needed, rate limits, error handling, or what 'details' include. This is insufficient for a tool with no annotation coverage, as it leaves key operational aspects undefined.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently communicates the core function, earning its place without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It does not explain what 'details' are returned, how errors are handled, or any behavioral context. For a tool that retrieves specific data, this leaves significant gaps in understanding its full operation and output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the parameter 'prompt_name' clearly documented as 'Name of the prompt template to get'. The description does not add any additional meaning beyond this, such as format examples or constraints. Given the high schema coverage, a baseline score of 3 is appropriate as the schema adequately handles parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get details of a specific prompt template', which includes a specific verb ('Get') and resource ('prompt template'). However, it does not explicitly distinguish this from sibling tools like 'get_source_prompts' or 'list_amazon_vpc_lattice_prompts', which likely involve similar resources but different scopes or operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 does not mention sibling tools, prerequisites, or specific contexts for usage, leaving the agent to infer based on the tool name alone. This lack of explicit when-to-use or when-not-to-use information reduces clarity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_source_promptsB

Get sample prompts for a specific source

ParametersJSON Schema

Name	Required	Description	Default
`source_name`	Yes	Name of the source to get prompts for

TDQS

B3.1/5.0

Behavior2/5

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 what the tool does but doesn't describe behavioral traits such as whether it's read-only, if it requires authentication, rate limits, error handling, or what format the 'sample prompts' are returned in. This leaves significant gaps for a tool that likely retrieves data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and output format, which are important for effective tool invocation in a broader context with sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 'source_name' clearly documented in the schema. The description adds minimal value beyond the schema by implying the parameter is used to identify a source, but it doesn't provide additional context like valid source names or examples. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get sample prompts') and the target resource ('for a specific source'), which provides a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_amazon_vpc_lattice_prompts' or 'list_sources', which appear to be related to similar domains but have different scopes or functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 'list_sources' (which might list sources before selecting one) or 'get_amazon_vpc_lattice_prompts' (which seems source-specific), leaving the agent to infer usage context without explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_amazon_vpc_lattice_promptsB

List all available prompt templates

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.2/5.0

Behavior2/5

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. While 'List all available prompt templates' implies a read-only operation, it doesn't specify whether this requires authentication, has rate limits, returns paginated results, or details the format of the output. For a tool with zero annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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 is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, no annotations, and no output schema, the description is minimally adequate but lacks completeness. It doesn't explain what 'prompt templates' are, how they're structured, or what the output looks like, which could hinder an agent's ability to use this tool effectively in context with siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary information beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('all available prompt templates'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_amazon_vpc_lattice_prompts' or 'get_source_prompts', which likely retrieve specific prompts rather than listing all templates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. The description doesn't mention sibling tools like 'get_amazon_vpc_lattice_prompts' (which might retrieve specific prompts) or 'list_sources' (which might list different resources), leaving the agent without context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_sourcesB

List all available sources with their URLs and sample prompts

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.2/5.0

Behavior2/5

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 mentions that the tool lists sources with URLs and sample prompts, which implies a read-only operation, but doesn't specify if this requires authentication, how data is returned (e.g., pagination, format), or any rate limits. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence: 'List all available sources with their URLs and sample prompts.' It is front-loaded with the core action and includes no unnecessary words, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there are no parameters and no output schema, the description provides a clear purpose but lacks details on behavioral aspects like authentication, return format, or error handling. For a simple list tool, this might be adequate, but without annotations or output schema, it doesn't fully prepare an agent for invocation, leaving room for improvement in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description doesn't need to add parameter details, so it appropriately focuses on the tool's purpose. A baseline of 4 is applied since no parameters exist, and the description doesn't attempt to explain non-existent inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'List all available sources with their URLs and sample prompts.' It specifies the verb ('List'), resource ('available sources'), and what information is included ('URLs and sample prompts'). However, it doesn't explicitly distinguish this from sibling tools like 'get_source_prompts' or 'list_amazon_vpc_lattice_prompts', which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. With sibling tools like 'get_source_prompts' and 'list_amazon_vpc_lattice_prompts' available, there is no indication of when this tool is appropriate, what prerequisites might be needed, or any exclusions for its use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

vpc_lattice_cliC

Execute AWS CLI VPC Lattice commands

ParametersJSON Schema

Name	Required	Description	Default
`args`	No	Command arguments as key-value pairs
`command`	Yes	The VPC Lattice subcommand to execute (e.g., create-service-network, list-service-networks)
`profile`	No	AWS CLI profile to use	default
`region`	No	AWS region	us-east-1

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions 'execute' which implies mutation, but doesn't disclose behavioral traits like which commands are destructive (e.g., delete-*), authentication needs, error handling, or output format. This is a significant gap for a CLI tool with potentially destructive operations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's function without unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (4 parameters, no annotations, no output schema, and a wide range of commands including destructive ones), the description is incomplete. It lacks context on safety, output, error cases, or how to interpret results, making it inadequate for effective tool use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents parameters. The description adds no meaning beyond the schema—it doesn't explain parameter relationships, command-argument mappings, or usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Execute AWS CLI VPC Lattice commands' states the action (execute) and target (AWS CLI VPC Lattice commands), but is vague about what VPC Lattice is and doesn't differentiate from sibling tools like get_amazon_vpc_lattice_prompts or list_amazon_vpc_lattice_prompts. It provides a basic purpose but lacks specificity about the resource domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. The description doesn't mention sibling tools, prerequisites like AWS credentials, or typical use cases. Usage is implied only through the command enum in the schema, not in the description itself.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesv1.0.0Aug 14, 2025
- First observedget_amazon_vpc_lattice_prompts
- First observedget_source_prompts
- First observedlist_amazon_vpc_lattice_prompts
- First observedlist_sources
- First observedvpc_lattice_cli

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2.9/5.0

Scored 2026-04-18 10:10 across 5 tools

Disambiguation3/5

The tools have some overlap but descriptions help clarify distinctions. 'get_amazon_vpc_lattice_prompts' and 'get_source_prompts' both retrieve prompts but target different entities (templates vs. sources), while 'list_amazon_vpc_lattice_prompts' and 'list_sources' similarly list different items. The 'vpc_lattice_cli' tool stands apart for CLI execution, but the prompt-related tools could cause mild confusion without careful reading.

Naming Consistency4/5

The naming is mostly consistent with a verb_noun pattern, using 'get_' and 'list_' prefixes clearly. However, 'vpc_lattice_cli' deviates by omitting a verb and using a compound noun, breaking the pattern. The other four tools follow a predictable convention, making this a minor inconsistency.

Tool Count3/5

With 5 tools, the count is borderline for the server's purpose of managing Amazon VPC Lattice prompts and sources. It feels slightly thin, as it covers listing and getting prompts/sources and CLI execution, but might lack operations like creating, updating, or deleting prompts, which could limit functionality. The scope is reasonable but not fully fleshed out.

Completeness2/5

There are significant gaps in the tool surface for managing Amazon VPC Lattice prompts. The server only provides read operations (get and list) for prompts and sources, along with CLI execution, but lacks create, update, or delete tools. This incomplete CRUD coverage will likely cause agent failures when full lifecycle management is needed, as agents cannot modify or add new prompts or sources.

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

AWS Knowledge A
aws.api.global.knowledge-mcp
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
Documentation Access Cloud Platforms Search
52
hithereiamaliff-mcp-nextcloudOAuth
ai.smithery
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Cloud Storage File Systems Workplace & Productivity
237
mcp COAuth
com.wix
A Model Context Protocol server for Wix AI tools
Developer Tools Content Management Systems App Automation
216
Sentry AOAuth
dev.sentry.mcp
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
Monitoring Developer Tools Observability
3

Related MCP Servers

Columbia MCP Server
Virtualization Observability Agent Orchestration
clavia-labs
F
license
Not graded
quality
Not graded
maintenance
Provides a scalable, containerized infrastructure for deploying and managing Model Context Protocol servers with monitoring, high availability, and secure configurations.
Updated 2 years ago (2025-01-27 01:53 UTC)
-
Log Analyzer with MCPofficial
Monitoring Observability Cloud Platforms
awslabs
A
license
Not graded
quality
F
maintenance
A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.
Updated a month ago (2026-08-05 08:27 UTC)
167
Apache 2.0
Amazon Leadership Principles
dminhk
-
license
Not graded
quality
Not graded
maintenance
Serves as a Model Context Protocol server that provides tools to look up Amazon Leadership Principles and access video transcripts for integration with Amazon Q CLI.
Updated a year ago (2025-06-14 01:47 UTC)
-
Swagger/OpenAPI MCP Server
API Testing Documentation Access
gulbaki
A
license
D
quality
D
maintenance
A Model Context Protocol server that enables LLMs to explore and interact with API specifications by providing tools for loading, browsing, and getting detailed information about API endpoints.
Updated a year ago (2025-06-16 14:03 UTC)
4
15
14
ISC

Amazon VPC Lattice MCP 서버

특징

설치

구성

용법

소스 목록

소스 프롬프트 가져오기

Amazon VPC Lattice 프롬프트 목록

Amazon VPC Lattice 프롬프트 세부 정보 가져오기

VPC 격자 CLI

특징

사용 가능한 명령

예시

사용 가능한 소스

개발

프로젝트 구조

새로운 소스 추가

새로운 프롬프트 추가

스크립트

Available Tools

Tool Schema Changelog

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Log Analyzer with MCPofficial