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licenseB
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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
股线代理 MCP
用于执行 Strands 代理的模型上下文协议 (MCP) 服务器。该项目提供了一种将 Strands 代理与 Amazon Q 及其他兼容 MCP 的系统集成的简单方法。
概述
Strands Agent MCP 是 Strands 代理框架和模型上下文协议 (MCP) 之间的桥梁。它允许您：
将 Strands 代理注册为 MCP 工具
通过 MCP 执行 Strands 代理
发现并列出可用的代理
该项目采用插件架构，可以轻松添加新代理，而无需修改核心代码。
Related MCP server: A2A Client MCP Server
安装
pip install strands-agent-mcp用法
启动 MCP 服务器
strands-agent-mcp这将在默认端口上启动 MCP 服务器。
创建代理插件
要创建新的代理插件，请创建一个以sap_mcp_plugin_开头的 Python 包（sap 代表 strands agent plugin）。您的包应该实现一个register_plugin函数，该函数用于向提供的注册表注册一个或多个代理：
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")))
)与 Amazon Q 一起使用
一旦 MCP 服务器运行，您就可以将代理与 Amazon Q 一起使用：
q chat --mcp-server http://localhost:8000然后您可以在聊天中使用以下命令：
列出可用的代理：
strands___list_agentslist_agents执行代理：
strands___execute_agentexecute_agent，参数为agent（代理名称）和prompt（发送给代理的提示）
建筑学
该项目由三个主要部分组成：
服务器：暴露代理执行 API 的 MCP 服务器
注册表：用于管理可用代理的简单注册表
插件：动态发现向注册表注册代理的模块
服务器自动发现所有遵循命名约定的已安装插件并注册其代理。
依赖项
fastmcp：用于实现 MCP 服务器strands-agents：核心 Strands 代理框架strands-agents-builder：用于构建 Strands 代理的工具strands-agents-tools：Strands 代理的附加工具
发展
要设置开发环境：
克隆存储库
创建虚拟环境：
python -m venv .venv激活虚拟环境：
source .venv/bin/activate（Linux/Mac）或.venv\Scripts\activate（Windows）安装开发依赖项：
pip install -e ".[dev]"
创建测试插件
该存储库包含一个示例插件（ sap_mcp_plugin_test ），它演示了如何创建和注册一个名为“simple-agent”的简单代理：
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")))
)执照
[在此处添加许可信息]
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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