F
licenseB
qualityD
maintenanceA Model Context Protocol server that enables Claude users to access specialized OpenAI agents (web search, file search, computer actions) and a multi-agent orchestrator through the MCP protocol.
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Strands Agent MCP
by imgaray
스트랜드 에이전트 MCP
Strands 에이전트를 실행하기 위한 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 프로젝트는 Strands 에이전트를 Amazon Q 및 기타 MCP 호환 시스템과 통합하는 간단한 방법을 제공합니다.
개요
Strands Agent MCP는 Strands 에이전트 프레임워크와 모델 컨텍스트 프로토콜(MCP)을 연결하는 브리지입니다. 다음과 같은 기능을 제공합니다.
Strands 에이전트를 MCP 도구로 등록
MCP를 통해 Strands 에이전트 실행
사용 가능한 에이전트를 검색하고 나열하세요
이 프로젝트는 핵심 코드를 수정하지 않고도 새로운 에이전트를 쉽게 추가할 수 있는 플러그인 아키텍처를 사용합니다.
Related MCP server: A2A Client MCP Server
설치
지엑스피1
용법
MCP 서버 시작
strands-agent-mcp이렇게 하면 기본 포트에서 MCP 서버가 시작됩니다.
에이전트 플러그인 생성
새 에이전트 플러그인을 만들려면 sap_mcp_plugin_ 으로 시작하는 Python 패키지를 만드세요(sap은 strands agent plugin의 약자입니다). 패키지는 제공된 레지스트리에 하나 이상의 에이전트를 등록하는 register_plugin 함수를 구현해야 합니다.
from strands import Agent
from strands.models import BedrockModel
from strands_agent_mcp.registry import Registry
def register_plugin(registry: Registry) -> None:
registry.register("my-agent", Agent(
model=BedrockModel(boto_session=Session(region_name="us-west-2")))
)Amazon Q와 함께 사용
MCP 서버가 실행되면 Amazon Q에서 에이전트를 사용할 수 있습니다.
q chat --mcp-server http://localhost:8000그러면 채팅에서 다음 명령을 사용할 수 있습니다.
사용 가능한 에이전트 목록:
strands___list_agents에이전트 실행:
strands___execute_agent매개변수(agent(에이전트 이름) 및prompt(에이전트에 보낼 프롬프트))
건축학
이 프로젝트는 세 가지 주요 구성 요소로 구성됩니다.
서버 : 에이전트 실행 API를 노출하는 MCP 서버
레지스트리 : 사용 가능한 에이전트를 관리하기 위한 간단한 레지스트리
플러그인 : 레지스트리에 에이전트를 등록하는 동적으로 검색된 모듈
서버는 명명 규칙을 따르는 모든 설치된 플러그인을 자동으로 검색하고 해당 에이전트를 등록합니다.
종속성
fastmcp: MCP 서버 구현용strands-agents: 핵심 Strands 에이전트 프레임워크strands-agents-builder: Strands 에이전트를 구축하기 위한 도구strands-agents-tools: Strands 에이전트를 위한 추가 도구
개발
개발 환경을 설정하려면:
저장소를 복제합니다
가상 환경을 만듭니다:
python -m venv .venv가상 환경을 활성화합니다:
source .venv/bin/activate(Linux/Mac) 또는.venv\Scripts\activate(Windows)개발 종속성 설치:
pip install -e ".[dev]"
테스트 플러그인 만들기
저장소에는 "simple-agent"라는 간단한 에이전트를 생성하고 등록하는 방법을 보여주는 샘플 플러그인( sap_mcp_plugin_test )이 포함되어 있습니다.
from boto3 import Session
from strands import Agent
from strands.models import BedrockModel
from strands_agent_mcp.registry import Registry
def register_plugin(registry: Registry) -> None:
registry.register("simple-agent", Agent(
model=BedrockModel(boto_session=Session(region_name="us-west-2")))
)특허
[여기에 라이센스 정보를 추가하세요]
Available Tools
3 toolsexecute_agentC
Execute an agent with a given prompt
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| agent_name | Yes | The name of the agent to execute | |
| prompt | Yes | The prompt to execute the agent with |
TDQS
C2.7/5.0
Behavior2/5
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 the action ('execute') but doesn't explain what execution involves (e.g., whether it runs a process, returns output, has side effects, requires permissions, or has rate limits). This leaves critical behavioral traits unspecified for a tool that likely performs a significant operation.
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 wasted words. It's front-loaded with the core action and parameters, making it easy to parse quickly. Every word earns its place, adhering to best practices for brevity.
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 of executing an agent (likely a non-trivial operation), no annotations, and no output schema, the description is incomplete. It fails to explain what happens during execution, what the output might be, or any behavioral context, leaving significant gaps for an AI agent to understand and use the tool effectively.
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, clearly documenting both parameters ('agent_name' and 'prompt'). The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 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 an agent with a given prompt' clearly states the verb ('execute') and resource ('agent'), but it's vague about what execution entails (e.g., running a task, generating a response). It doesn't differentiate from sibling tools like 'list_agents' or 'list_skills', which are read-only listing operations, but the distinction is implied rather than explicit.
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 prerequisites (e.g., needing an existing agent), exclusions, or how it relates to siblings like 'list_agents' for selecting an agent to execute. Usage is implied from the name and parameters but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_agentsC
list all available agents
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 ('list all available agents') without revealing any behavioral traits such as whether it's read-only, if it requires authentication, how results are returned (e.g., pagination, format), or any rate limits. This is a significant gap for a tool with no annotation coverage.
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 'list all available agents' is a single, efficient sentence that front-loads the core action and resource. It has zero waste, making it appropriately sized for a simple listing tool. Every word earns its place by conveying the essential purpose without redundancy.
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 tool's simplicity (0 parameters, no output schema, no annotations), the description is minimal but incomplete. It lacks context about what 'agents' are, how the listing is structured (e.g., as a list, array, or paginated), and behavioral details. For a tool with no structured data to rely on, the description should provide more completeness to aid the agent effectively.
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 tool has 0 parameters, and the schema description coverage is 100% (as there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters. No additional value is required beyond stating the purpose.
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 'list all available agents' clearly states the verb ('list') and resource ('agents'), making the purpose understandable. However, it lacks specificity about what 'agents' are in this context and doesn't distinguish from sibling tools like 'list_skills', which suggests a similar listing operation but for different resources. The description is functional but generic.
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 'execute_agent' (which likely performs actions with agents) or 'list_skills' (which lists a different resource), leaving the agent to infer usage based on tool names alone. There's no explicit context or exclusions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_skillsB
list all available skills for agents
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 the tool lists skills but doesn't describe behavioral traits such as whether it's read-only, requires authentication, has rate limits, or what the output format looks like. For a tool with zero annotation coverage, this is a significant gap in transparency.
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. Every part of the sentence earns its place by conveying essential information.
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 tool's simplicity (0 parameters, no output schema), the description is minimal but adequate for basic understanding. However, it lacks context about behavioral aspects (e.g., read-only status, output format) and doesn't differentiate from siblings, making it incomplete for optimal agent guidance. With no annotations and no output schema, more detail would be beneficial.
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 tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics beyond what the schema provides. A baseline of 4 is appropriate as it avoids redundancy while being complete for a parameterless tool.
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 skills for agents'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings (execute_agent and list_agents), which would require a 5. The description avoids tautology by not merely restating the tool name.
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 its siblings (execute_agent and list_agents) or any alternatives. It implies usage for retrieving skill information but lacks explicit context, prerequisites, or exclusions, leaving the agent with minimal direction.
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.
3 tool updates
v1.0.0- First observed
execute_agent - First observed
list_agents - First observed
list_skills
TDQS
B3.1/5.0
Scored across 3 tools
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: execute_agent runs an agent, list_agents enumerates available agents, and list_skills enumerates available skills. The descriptions make it unambiguous which tool to use for each operation.
Naming Consistency5/5
All three tools follow a consistent verb_noun pattern (execute_agent, list_agents, list_skills) with the same naming convention throughout. The verbs (execute, list) are appropriate and consistently applied.
Tool Count3/5
With only 3 tools, the set feels thin for an agent management system. While the tools cover basic operations (list and execute), there are likely missing capabilities like creating, updating, or deleting agents or skills, which limits the server's scope.
Completeness2/5
The tool surface is significantly incomplete for agent management. It lacks essential CRUD operations (e.g., create_agent, update_agent, delete_agent, create_skill) and other lifecycle actions (e.g., stop_agent, monitor_agent). This will cause agent failures when trying to perform basic management tasks.
Maintenance
ActivityInactive
ResponsivenessNo issues
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