这是一个连接到 Brightsy AI 代理的模型上下文协议 (MCP) 服务器。
npm installRelated MCP server: AI Helper MCP Server
要启动服务器：
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key>或者使用位置参数：
npm start -- <your-agent-id> <your-api-key> [tool-name] [message]您还可以提供要发送给代理的初始消息：
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key> --message="Hello, agent!"默认情况下，MCP 服务器会注册一个名为“brightsy”的工具。您可以使用--tool-name参数自定义此名称：
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key> --tool-name=<custom-tool-name>您还可以将工具名称设置为第三个位置参数：
npm start -- <your-agent-id> <your-api-key> <custom-tool-name>或者使用BRIGHTSY_TOOL_NAME环境变量：
export BRIGHTSY_TOOL_NAME=custom-tool-name
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key>以下环境变量可用于配置服务器：
BRIGHTSY_AGENT_ID ：要使用的代理 ID（命令行参数的替代）
BRIGHTSY_API_KEY ：要使用的 API 密钥（命令行参数的替代）
BRIGHTSY_TOOL_NAME ：要注册的工具名称（默认值：“brightsy”）
agent_proxy 工具允许您将请求代理到 Brightsy AI 代理。要测试此工具，您可以使用提供的测试脚本。
在运行测试之前，请设置以下环境变量：
export AGENT_ID=your-agent-id
export API_KEY=your-api-key
# Optional: customize the tool name for testing
export TOOL_NAME=custom-tool-name或者，您可以将这些值作为命令行参数传递：
# 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-name运行所有测试：
npm test运行特定测试：
# Test using the command line interface
npm run test:cli
# Test using the direct MCP protocol
npm run test:direct命令行测试（ test-agent-proxy.ts ）：通过使用测试消息运行 MCP 服务器来测试 agent_proxy 工具。
直接 MCP 协议测试（ test-direct.ts ）：通过直接向服务器发送格式正确的 MCP 请求来测试 agent_proxy 工具。
MCP 服务器注册了一个工具（默认名为“brightsy”），用于将请求转发给兼容 OpenAI 的 AI 代理并返回响应。该工具接受一个messages参数，该参数是一个包含role和content属性的消息对象数组。
MCP 客户端中的示例用法：
// 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?"
}
]
});响应将在content字段中包含代理的回复。
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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