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jamesbrink

MCP Server for Coroot

test_integration

Verify integration configuration and connectivity for Coroot observability platform projects. Tests connections to services like Prometheus or Slack.

Instructions

Test an integration configuration.

Verifies that an integration is properly configured and can connect.

Args: project_id: Project ID integration_type: Type of integration (prometheus, slack, etc)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
integration_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Registration of the 'test_integration' MCP tool via @mcp.tool() decorator, with handler function that wraps the implementation.
    @mcp.tool()
    async def test_integration(project_id: str, integration_type: str) -> dict[str, Any]:
        """Test an integration configuration.
    
        Verifies that an integration is properly configured and can connect.
    
        Args:
            project_id: Project ID
            integration_type: Type of integration (prometheus, slack, etc)
        """
        return await test_integration_impl(project_id, integration_type)  # type: ignore[no-any-return]
  • Wrapper handler implementation that calls the CorootClient.test_integration method and formats the response.
    @handle_errors
    async def test_integration_impl(
        project_id: str, integration_type: str
    ) -> dict[str, Any]:
        """Test an integration."""
        result = await get_client().test_integration(project_id, integration_type)
        return {
            "success": True,
            "message": f"{integration_type} integration test completed",
            "result": result,
        }
  • Core implementation in CorootClient that fetches current integration config and sends a test POST request to the Coroot API.
    async def test_integration(
        self, project_id: str, integration_type: str
    ) -> dict[str, Any]:
        """Test an integration configuration.
    
        Args:
            project_id: Project ID.
            integration_type: Type of integration to test.
    
        Returns:
            Test results.
        """
        # First get the current config
        integrations = await self.list_integrations(project_id)
        current_config = integrations.get(integration_type, {})
    
        # Send POST with current config to test it
        response = await self._request(
            "POST",
            f"/api/project/{project_id}/integrations/{integration_type}",
            json=current_config,
        )
        data: dict[str, Any] = response.json()
        return data
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 'verifies' connectivity, implying a read-only or diagnostic operation, but doesn't clarify if it's safe (non-destructive), what permissions are needed, whether it makes external calls, or what happens on failure. For a tool with no annotations, 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 concise and well-structured: a brief purpose statement followed by parameter explanations. Every sentence adds value without redundancy. It could be slightly more front-loaded by integrating parameter hints into the main description, but overall it's efficient and clear.

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's moderate complexity (2 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for output details in the description. However, it lacks usage context, behavioral transparency, and full parameter semantics, leaving gaps in completeness for a testing tool.

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?

Schema description coverage is 0%, so the schema provides no parameter details. The description adds basic semantics: 'project_id: Project ID' and 'integration_type: Type of integration (prometheus, slack, etc).' This clarifies what each parameter represents and gives examples for integration_type, partially compensating for the low coverage. However, it doesn't specify formats, constraints, or how they interact.

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: 'Test an integration configuration' and 'Verifies that an integration is properly configured and can connect.' This specifies the verb (test/verify) and resource (integration configuration), making it clear what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'configure_integration' or 'get_integration' beyond the testing aspect.

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 prerequisites (e.g., whether an integration must be configured first), compare it to sibling tools like 'configure_integration' or 'get_integration', or specify scenarios where testing is appropriate (e.g., after setup or troubleshooting). This lack of context leaves usage unclear.

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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