msio_revenue_forecast

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

Annotations already establish readOnly, openWorld, idempotent, and non-destructive traits. The description adds valuable behavioral context beyond that: projection uses a simple moving average (not ML), accuracy degrades with sparse data, and pipeline figures reflect scheduled jobs only. These gotchas help the agent set expectations and avoid misinterpreting results.

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 compact and highly structured, with clear USE WHEN, DO NOT USE WHEN, and GOTCHAS sections. Every sentence carries meaningful guidance, and the most important purpose statement is front-loaded. No filler or redundancy.

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?

With an output schema present, return-value details are not needed. The description covers purpose, usage boundaries, and key behavioral limitations. However, the meaning of 'pipeline' in 'Pipeline shows scheduled jobs only' is somewhat unclear, and employee_id filtering is not explained. These are minor gaps given the tool's overall simplicity and the presence of an output schema.

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 description coverage is 0%, so the description should compensate, but it only indirectly addresses parameters: 'Accuracy decreases with fewer months of data' hints at lookback_months. The names lookback_months and projection_months are self-explanatory, and employee_id has a default of null, but the description never explicitly maps parameters to behavior or clarifies how employee_id filters the analysis. This is a partial, not full, compensation.

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 opening line, 'Historical revenue analysis with trend detection and simple projection,' states a specific verb and resource, making the tool's function immediately clear. The DO NOT USE section further distinguishes it from siblings like hcp_get_job and hcp_list_invoices, so an agent can disambiguate without inspecting schemas.

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 USE WHEN list covers concrete intents ('How's business?', revenue trends, forecasting, seasonal analysis, growth questions, financial planning), while DO NOT USE WHEN explicitly names alternatives with conditions ('specific job revenue' → hcp_get_job, 'invoice details' → hcp_list_invoices). This gives the agent clear decision rules for selection.

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