generate_kpi_report

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

No annotations provided, so description must disclose behavior. It mentions computing metrics and formatting for delivery, but omits output format (e.g., JSON, PDF, dashboard), side effects (e.g., storage, persistence), or whether it is read-only. Critical gaps for an agent to anticipate consequences.

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?

Two succinct sentences: first states purpose and scope, second lists key metrics and audience. No extraneous information, all sentences earn their place.

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?

Despite clear purpose, crucial gaps remain: no output format described, no behavioral side effects disclosed, and no usage guidance. For a tool with 3 parameters and no output schema, more completeness is needed to guide correct invocation.

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?

Schema coverage is 100% with descriptions for all parameters. Description adds context that the tool computes latency, throughput, error rate, and SLA adherence, clarifying report content beyond schema fields. However, does not explain how parameters relate to computation (e.g., how sla_latency_ms is used). Baseline 3 appropriate.

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?

Explicitly states it generates a structured KPI performance report for financial data pipelines or AI agent runs, lists specific metrics (latency, throughput, error rate, SLA adherence), and notes formatting for senior stakeholder delivery. Clearly distinguishes from sibling tools like audit_data_quality or detect_bias.

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?

Implies usage when a KPI report is needed for a pipeline/agent run, but does not explicitly state when to use versus alternatives, nor provide when-not scenarios or prerequisites. Sibling context suggests differentiation but description itself lacks direct guidance.

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