Este es un servidor de Protocolo de Contexto de Modelo (MCP) que se conecta a un agente de Brightsy AI.
npm installRelated MCP server: AI Helper MCP Server
Para iniciar el servidor:
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key>O con argumentos posicionales:
npm start -- <your-agent-id> <your-api-key> [tool-name] [message]También puedes proporcionar un mensaje inicial para enviar al agente:
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key> --message="Hello, agent!"De forma predeterminada, el servidor MCP registra una herramienta llamada "brightsy". Puede personalizar este nombre con el parámetro --tool-name :
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key> --tool-name=<custom-tool-name>También puede establecer el nombre de la herramienta como el tercer argumento posicional:
npm start -- <your-agent-id> <your-api-key> <custom-tool-name>O utilizando la variable de entorno BRIGHTSY_TOOL_NAME :
export BRIGHTSY_TOOL_NAME=custom-tool-name
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key>Las siguientes variables de entorno se pueden utilizar para configurar el servidor:
BRIGHTSY_AGENT_ID : El ID del agente a utilizar (alternativa al argumento de la línea de comandos)
BRIGHTSY_API_KEY : La clave API a utilizar (alternativa al argumento de la línea de comandos)
BRIGHTSY_TOOL_NAME : El nombre de la herramienta a registrar (predeterminado: "brightsy")
La herramienta agent_proxy permite redirigir solicitudes a un agente de Brightsy AI. Para probar esta herramienta, puede usar los scripts de prueba proporcionados.
Antes de ejecutar las pruebas, configure las siguientes variables de entorno:
export AGENT_ID=your-agent-id
export API_KEY=your-api-key
# Optional: customize the tool name for testing
export TOOL_NAME=custom-tool-nameAlternativamente, puede pasar estos valores como argumentos de línea de comando:
# Using named arguments
npm run test:cli -- --agent-id=your-agent-id --api-key=your-api-key --tool-name=custom-tool-name
# Using positional arguments
npm run test:cli -- your-agent-id your-api-key custom-tool-namePara ejecutar todas las pruebas:
npm testPara ejecutar pruebas específicas:
# Test using the command line interface
npm run test:cli
# Test using the direct MCP protocol
npm run test:directPrueba de línea de comandos ( test-agent-proxy.ts ): prueba la herramienta agent_proxy ejecutando el servidor MCP con un mensaje de prueba.
Prueba de protocolo MCP directa ( test-direct.ts ): prueba la herramienta agent_proxy enviando una solicitud MCP con el formato correcto directamente al servidor.
El servidor MCP registra una herramienta (denominada "brightsy" por defecto) que reenvía las solicitudes a un agente de IA compatible con OpenAI y devuelve la respuesta. Recibe un parámetro messages , que es una matriz de objetos de mensaje con propiedades role y content .
Ejemplo de uso en un cliente MCP:
// Using the default tool name
const response = await client.callTool("brightsy", {
messages: [
{
role: "user",
content: "Hello, can you help me with a simple task?"
}
]
});
// Or using a custom tool name if configured
const response = await client.callTool("custom-tool-name", {
messages: [
{
role: "user",
content: "Hello, can you help me with a simple task?"
}
]
});La respuesta contendrá la respuesta del agente en el campo content .
Proxy requests to an Brightsy AI agent
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | The messages to send to the agent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'proxy requests' which implies some form of communication forwarding, but doesn't describe authentication requirements, rate limits, error handling, response format, or what the Brightsy AI agent actually does. This leaves significant behavioral gaps for a proxying tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core purpose and contains no unnecessary elaboration. This is an example of efficient communication that earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a proxying tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the Brightsy AI agent is, what types of requests are proxied, what authentication is needed, or what format the responses take. The combination of vague purpose and missing behavioral context creates significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single 'messages' parameter with its structure. The description adds no additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'proxy requests to an Brightsy AI agent', which provides a basic verb+resource combination. However, it's vague about what 'proxy requests' specifically entails - whether it's for chat, API calls, or other interactions. Without sibling tools, differentiation isn't needed, but the purpose lacks specificity about the nature of the proxying.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, nor any context about prerequisites or appropriate scenarios. With no sibling tools, the absence of explicit 'when-not-to-use' guidance is less critical, but there's still no usage context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0brightsyScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'brightsy' has a clear and distinct purpose as a proxy to the Brightsy AI agent.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'brightsy' is straightforward and matches the server's purpose.
A single tool is generally too few for most server purposes, as it offers minimal functionality and can limit agent capabilities. While it might suffice for a simple proxy, it feels thin and under-scoped for typical MCP server expectations.
The tool surface is incomplete for a general-purpose AI agent proxy, lacking operations like configuration, status checks, or specific request types. However, the single tool covers the basic proxy function, leaving notable gaps but not entirely failing.
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