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server_health_check

Check server and Azure DevOps connection health to ensure reliable development workflow operations.

Instructions

Performs a comprehensive health check of the server and Azure DevOps connection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Executes the server_health_check tool logic by validating server environment, client status, tool registration, and testing Azure DevOps connection via get_projects().
    async def _health_check(self) -> Dict[str, Any]:
        """Perform comprehensive health check."""
        health_status = {
            "server_status": "healthy",
            "environment_check": self._validate_environment(),
            "client_initialized": self.client is not None,
            "tools_registered": self.tools_registered,
            "total_tools": len(self.tools),
            "azure_devops_connection": "unknown"
        }
        
        if self.client:
            try:
                # Test Azure DevOps connection
                projects = self.client.get_projects()
                health_status["azure_devops_connection"] = "connected"
                health_status["available_projects"] = len(projects)
            except Exception as e:
                health_status["azure_devops_connection"] = f"error: {str(e)}"
        
        return health_status
  • Defines the input schema, description, and registers the server_health_check tool in the self.tools list.
    types.Tool(
        name="server_health_check",
        description="Performs a comprehensive health check of the server and Azure DevOps connection.",
        inputSchema={
            "type": "object",
            "properties": {},
            "additionalProperties": False
        }
    ),
  • Dispatches the server_health_check tool call to the _health_check handler in _execute_tool method.
    if name == "server_health_check":
        return await self._health_check()
  • Helper code in setup validation script that checks for presence of server_health_check among key tools.
    key_tools = ['create_work_item', 'get_work_item', 'create_wiki_page', 'server_health_check']
    found_tools = [tool.name for tool in server.tools if tool.name in key_tools]
    
    for tool in found_tools:
        self.log_info(f"  ✓ {tool}")
    
    missing_tools = set(key_tools) - set(found_tools)
    for tool in missing_tools:
        self.log_warning(f"  ✗ {tool} (not found)")
    
    return tools_count > 0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions 'comprehensive health check' but doesn't disclose what that entails—whether it's read-only, what metrics it returns, if it impacts server performance, or if authentication is required. For a tool with zero annotation coverage, this leaves critical behavioral traits unspecified.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Performs a comprehensive health check of the server and Azure DevOps connection') directly contributes to understanding the tool's function, making it optimally concise.

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 complexity (diagnostic with no parameters) and lack of annotations and output schema, the description is minimally adequate. It states what the tool does but doesn't explain what a 'health check' returns or any behavioral implications. For a tool with no structured output documentation, more detail on expected results would improve completeness.

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 absence of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as no additional semantic detail is needed beyond what the schema provides.

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 ('Performs') and target ('comprehensive health check of the server and Azure DevOps connection'). It distinguishes from sibling tools by focusing on system diagnostics rather than wiki, work item, or project operations. However, it doesn't explicitly differentiate from potential similar monitoring tools that might exist.

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., server availability), frequency recommendations, or what scenarios warrant a health check versus using specific diagnostic tools. With many sibling tools for data operations, context for choosing this diagnostic tool is missing.

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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