servicenow-mcp

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. While it mentions what the tool does ('identify process bottlenecks'), it doesn't describe important behavioral aspects: whether this is a read-only analysis or makes changes, what permissions are required, whether it's resource-intensive, what format the output takes, or any rate limits. For an ML analysis tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence that gets straight to the point: 'Identify process bottlenecks using analysis of task durations and reassignment patterns.' Every word contributes to understanding the tool's purpose, with zero wasted verbiage. It's appropriately sized and front-loaded with the core functionality.

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?

Given that this is an ML analysis tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of output to expect (report, visualization, list of bottlenecks), how results are formatted, whether this is a computationally intensive operation, or what permissions might be required. For a tool that performs complex analysis, more context about the behavioral characteristics and output would be needed.

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?

The schema description coverage is 100%, so the schema already fully documents both parameters ('table' and 'days'). The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'analysis of task durations and reassignment patterns' which relates to what the tool analyzes, but doesn't provide additional context about parameter usage, constraints, or examples. The baseline 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 clearly states the tool's purpose: 'Identify process bottlenecks using analysis of task durations and reassignment patterns.' It specifies the verb ('identify') and resource ('process bottlenecks'), and mentions the analytical methods used. However, it doesn't explicitly distinguish this tool from sibling ML tools like ml_detect_anomalies or ml_forecast_incidents, 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.

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, appropriate contexts, or when other tools might be more suitable. With numerous sibling tools available, including other ML analysis tools, this lack of differentiation leaves the agent without usage guidance.

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