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
ストランドエージェントMCP
Strandsエージェントを実行するためのモデルコンテキストプロトコル（MCP）サーバー。このプロジェクトは、StrandsエージェントをAmazon Qやその他のMCP互換システムと簡単に統合する方法を提供します。
概要
Strands Agent MCPは、Strandsエージェントフレームワークとモデルコンテキストプロトコル（MCP）間のブリッジです。これにより、以下のことが可能になります。
StrandsエージェントをMCPツールとして登録する
MCP を通じて Strands エージェントを実行する
利用可能なエージェントの検出とリスト
このプロジェクトでは、コアコードを変更せずに新しいエージェントを簡単に追加できるプラグイン アーキテクチャを使用しています。
Related MCP server: A2A Client MCP Server
インストール
pip install strands-agent-mcp使用法
MCPサーバーの起動
strands-agent-mcpこれにより、デフォルトのポートで MCP サーバーが起動します。
エージェントプラグインの作成
新しいエージェントプラグインを作成するには、名前がsap_mcp_plugin_で始まる Python パッケージを作成します (sap は strands agent plugin の略です)。パッケージには、指定されたレジストリに 1 つ以上のエージェントを登録するregister_plugin関数を実装する必要があります。
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 Qでの使用
MCP サーバーが実行される場合は、Amazon Q でエージェントを使用できます。
q chat --mcp-server http://localhost:8000その後、チャットで次のコマンドを使用できます。
利用可能なエージェントの一覧:
strands___list_agentsエージェントを実行します:
strands___execute_agentパラメータagent(エージェント名) とprompt(エージェントに送信するプロンプト)
建築
このプロジェクトは、次の 3 つの主要コンポーネントで構成されています。
サーバー: エージェント実行APIを公開するMCPサーバー
レジストリ: 利用可能なエージェントを管理するためのシンプルなレジストリ
プラグイン: エージェントをレジストリに登録する動的に検出されたモジュール
サーバーは、命名規則に従うすべてのインストール済みプラグインを自動的に検出し、そのエージェントを登録します。
依存関係
fastmcp: MCPサーバーの実装用strands-agents: Strands エージェント フレームワークのコアstrands-agents-builder: Strandsエージェントを構築するためのツールstrands-agents-tools: Strandsエージェント用の追加ツール
発達
開発環境をセットアップするには:
リポジトリをクローンする
仮想環境を作成する:
python -m venv .venv仮想環境をアクティブ化します:
source .venv/bin/activate(Linux/Mac) または.venv\Scripts\activate(Windows)開発依存関係をインストールします:
pip install -e ".[dev]"
テストプラグインの作成
リポジトリには、「simple-agent」という単純なエージェントを作成して登録する方法を示すサンプル プラグイン ( sap_mcp_plugin_test ) が含まれています。
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")))
)ライセンス
[ここにライセンス情報を追加]
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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