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jamesbrink

MCP Server for Coroot

get_custom_applications

Retrieve custom application configurations from Coroot to group instances using defined patterns for monitoring and observability purposes.

Instructions

Get custom applications configuration.

Returns the list of custom application definitions that group instances by patterns.

Args: project_id: Project ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler implementation that performs the HTTP GET request to the Coroot API endpoint /api/project/{project_id}/custom_applications to fetch custom applications configuration.
    async def get_custom_applications(self, project_id: str) -> dict[str, Any]:
        """Get custom applications configuration.
    
        Args:
            project_id: Project ID.
    
        Returns:
            Custom applications.
        """
        response = await self._request(
            "GET", f"/api/project/{project_id}/custom_applications"
        )
        data: dict[str, Any] = response.json()
        return data
  • MCP server handler wrapper that calls the client method, adds success wrapper, and handles errors.
    async def get_custom_applications_impl(project_id: str) -> dict[str, Any]:
        """Get custom applications."""
        applications = await get_client().get_custom_applications(project_id)
        return {
            "success": True,
            "applications": applications,
        }
  • MCP tool registration using @mcp.tool() decorator, which exposes the function as the 'get_custom_applications' tool.
    @mcp.tool()
    async def get_custom_applications(project_id: str) -> dict[str, Any]:
        """Get custom applications configuration.
    
        Returns the list of custom application definitions that group
        instances by patterns.
    
        Args:
            project_id: Project ID
        """
        return await get_custom_applications_impl(project_id)  # type: ignore[no-any-return]
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 mentions returning a list of definitions, but doesn't describe whether this is a read-only operation, if it requires specific permissions, what happens on errors, or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with three sentences: purpose statement, return value explanation, and parameter documentation. It's front-loaded with the core functionality. While efficient, the 'Args' section could be integrated more smoothly rather than as a separate block.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (which handles return values) and only one parameter with partial documentation in the description, the description is minimally adequate. However, with no annotations and multiple sibling tools for similar data retrieval, it lacks context about when this specific tool is appropriate versus alternatives.

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 description includes an 'Args' section that documents the single parameter 'project_id', but with 0% schema description coverage, this only partially compensates. It explains what 'project_id' represents (Project ID), which adds meaning beyond the schema's title 'Project Id'. However, it doesn't provide format examples or constraints, leaving some semantic gaps.

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: 'Get custom applications configuration' and 'Returns the list of custom application definitions that group instances by patterns.' This specifies the verb (get/return), resource (custom applications configuration/definitions), and scope (list with grouping patterns). However, it doesn't explicitly differentiate from sibling tools like 'get_application' or 'get_applications_overview' which might retrieve similar data.

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. With sibling tools like 'get_application', 'get_applications_overview', and 'update_custom_applications', there's no indication of the specific context or prerequisites for selecting this tool over others. The description only states what it does, not when it should be used.

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

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