Skip to main content
Glama
jamesbrink

MCP Server for Coroot

get_ai_config

Retrieve AI provider configuration settings used for root cause analysis in the Coroot observability platform.

Instructions

Get AI provider configuration.

Retrieves AI provider settings used for root cause analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler method in CorootClient that executes the HTTP GET request to the /api/ai endpoint to retrieve the AI configuration.
    async def get_ai_config(self) -> dict[str, Any]:
        """Get AI provider configuration.
    
        Returns:
            AI provider configuration.
        """
        response = await self._request("GET", "/api/ai")
        data: dict[str, Any] = response.json()
        return data
  • MCP tool registration decorator and function definition that registers 'get_ai_config' as an MCP tool, delegating to the implementation.
    @mcp.tool()
    async def get_ai_config() -> dict[str, Any]:
        """Get AI provider configuration.
    
        Retrieves AI provider settings used for root cause analysis.
        """
        return await get_ai_config_impl()  # type: ignore[no-any-return]
  • Helper implementation function that wraps the client call, handles errors, and formats the response for the MCP tool.
    @handle_errors
    async def get_ai_config_impl() -> dict[str, Any]:
        """Get AI configuration."""
        client = get_client()
        config = await client.get_ai_config()
        return {
            "success": True,
            "config": config,
        }
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 'Retrieves' settings, implying a read-only operation, but doesn't specify permissions, rate limits, or what happens if no configuration exists. 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 front-loaded with the core purpose in the first sentence and adds clarifying context in the second. Both sentences are essential, with no wasted words, making it appropriately sized and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 annotations, but with an output schema), the description is reasonably complete. It explains what the tool does and the context of the configuration, though it could benefit from more behavioral details like error handling or output format hints, but the output schema mitigates this gap.

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 with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it doesn't introduce any confusion, earning a baseline score above 3 for compensating appropriately with no parameters.

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 tool's purpose with a specific verb ('Get') and resource ('AI provider configuration'), and it elaborates on what the configuration is used for ('root cause analysis'). However, it doesn't explicitly differentiate from sibling tools like 'update_ai_config' or other configuration-related tools, which prevents a perfect score.

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 when to use it (e.g., for checking current settings before updates) or when not to use it, nor does it refer to sibling tools like 'update_ai_config' for modifications, leaving the agent without usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jamesbrink/mcp-coroot'

If you have feedback or need assistance with the MCP directory API, please join our Discord server