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.
4
10
-
Strands Agent MCP
by imgaray
Agente de Strands MCP
Un servidor de Protocolo de Contexto de Modelo (MCP) para ejecutar agentes de Strands. Este proyecto proporciona una forma sencilla de integrar agentes de Strands con Amazon Q y otros sistemas compatibles con MCP.
Descripción general
El MCP del Agente Strands es un puente entre el marco del agente Strands y el Protocolo de Contexto de Modelo (MCP). Permite:
Registrar agentes de Strands como herramientas MCP
Ejecutar agentes de Strands a través de MCP
Descubra y enumere los agentes disponibles
El proyecto utiliza una arquitectura de complemento que facilita agregar nuevos agentes sin modificar el código central.
Related MCP server: A2A Client MCP Server
Instalación
pip install strands-agent-mcpUso
Iniciando el servidor MCP
strands-agent-mcpEsto iniciará el servidor MCP en el puerto predeterminado.
Creación de complementos de agente
Para crear un nuevo complemento de agente, cree un paquete de Python cuyo nombre comience por sap_mcp_plugin_ (sap significa complemento de agente de strands). Su paquete debe implementar una función register_plugin que registre uno o más agentes en el registro proporcionado:
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")))
)Uso con Amazon Q
Una vez que el servidor MCP esté en ejecución, puedes usar los agentes con Amazon Q:
q chat --mcp-server http://localhost:8000Luego podrás utilizar los siguientes comandos en tu chat:
Lista de agentes disponibles:
strands___list_agentsEjecutar un agente:
strands___execute_agentcon los parámetrosagent(nombre del agente) yprompt(el mensaje que se enviará al agente)
Arquitectura
El proyecto consta de tres componentes principales:
Servidor : El servidor MCP que expone la API de ejecución del agente
Registro : Un registro simple para administrar los agentes disponibles
Complementos : módulos descubiertos dinámicamente que registran agentes en el registro
El servidor descubre automáticamente todos los complementos instalados que siguen la convención de nomenclatura y registra sus agentes.
Dependencias
fastmcp: Para implementar el servidor MCPstrands-agents: El marco principal del agente Strandsstrands-agents-builder: Herramientas para crear agentes de Strandsstrands-agents-tools: Herramientas adicionales para agentes de Strands
Desarrollo
Para configurar un entorno de desarrollo:
Clonar el repositorio
Crear un entorno virtual:
python -m venv .venvActivar el entorno virtual:
source .venv/bin/activate(Linux/Mac) o.venv\Scripts\activate(Windows)Instalar dependencias de desarrollo:
pip install -e ".[dev]"
Creación de un complemento de prueba
El repositorio incluye un complemento de muestra ( sap_mcp_plugin_test ) que demuestra cómo crear y registrar un agente simple llamado "simple-agent":
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")))
)Licencia
[Agregue información de licencia aquí]
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
Related MCP Connectors
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server connecting AI agents to 100+ apps (Gmail, Slack, Notion, GitHub) via one-click OAuth.
Related MCP Servers
- FlicenseAqualityDmaintenanceAn MCP server that enables LLMs to interact with Agent-to-Agent (A2A) protocol compatible agents, allowing for sending messages, tracking tasks, and receiving streaming responses.528-
- FlicenseBqualityDmaintenanceA Model Context Protocol server implementation that can be run directly or through Docker, enabling AI assistants to interact with external systems through the MCP standard.2-
- AlicenseNot gradedqualityFmaintenanceA Model Context Protocol (MCP) server that enables multiple AI agents to share memory, coordinate tasks, and collaborate effectively across IDEs and CLI tools.1916MIT