The Brightsy MCP Server acts as a bridge to an OpenAI-compatible AI agent, allowing you to:
Proxy Requests: Forward requests from an MCP client to a Brightsy AI agent using a customizable tool name (default is "brightsy").
Handle Structured Input: Process standard chat format inputs with messages arrays containing role and content properties.
Configure Flexibly: Set up the server using either command-line arguments or environment variables.
Connect Securely: Use configurable credentials (agent ID and API key) to authenticate with the Brightsy AI agent.
Test Functionality: Utilize included scripts for both command-line and direct MCP protocol testing.
Allows forwarding requests to an Brightsy AI agent using an OpenAI-compatible format, enabling interaction with the agent through a standardized messages array with role and content properties.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Brightsy MCP Server Can you help me draft a professional email to schedule a meeting?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This is a Model Context Protocol (MCP) server that connects to an Brightsy AI agent.
npm installRelated MCP server: AI Helper MCP Server
To start the server:
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key>Or with positional arguments:
npm start -- <your-agent-id> <your-api-key> [tool-name] [message]You can also provide an initial message to be sent to the agent:
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key> --message="Hello, agent!"By default, the MCP server registers a tool named "brightsy". You can customize this name using the --tool-name parameter:
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key> --tool-name=<custom-tool-name>You can also set the tool name as the third positional argument:
npm start -- <your-agent-id> <your-api-key> <custom-tool-name>Or using the BRIGHTSY_TOOL_NAME environment variable:
export BRIGHTSY_TOOL_NAME=custom-tool-name
npm start -- --agent-id=<your-agent-id> --api-key=<your-api-key>The following environment variables can be used to configure the server:
BRIGHTSY_AGENT_ID: The agent ID to use (alternative to command line argument)
BRIGHTSY_API_KEY: The API key to use (alternative to command line argument)
BRIGHTSY_TOOL_NAME: The tool name to register (default: "brightsy")
The agent_proxy tool allows you to proxy requests to an Brightsy AI agent. To test this tool, you can use the provided test scripts.
Before running the tests, set the following environment variables:
export AGENT_ID=your-agent-id
export API_KEY=your-api-key
# Optional: customize the tool name for testing
export TOOL_NAME=custom-tool-nameAlternatively, you can pass these values as command-line arguments:
# 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-nameTo run all tests:
npm testTo run specific tests:
# Test using the command line interface
npm run test:cli
# Test using the direct MCP protocol
npm run test:directCommand Line Test (test-agent-proxy.ts): Tests the agent_proxy tool by running the MCP server with a test message.
Direct MCP Protocol Test (test-direct.ts): Tests the agent_proxy tool by sending a properly formatted MCP request directly to the server.
The MCP server registers a tool (named "brightsy" by default) that forwards requests to an OpenAI-compatible AI agent and returns the response. It takes a messages parameter, which is an array of message objects with role and content properties.
Example usage in an MCP client:
// 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?"
}
]
});The response will contain the agent's reply in the content field.
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.
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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