query_machine_history

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

No annotations exist, so the description carries full burden. It explains query options and notes summary mode is 'Always cheap'. However, it doesn't state that the tool is read-only or safe, which is implied but not explicit.

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 concise, well-structured with bullet points and a clear 'USE WHEN' section. Every sentence adds value without 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?

Given moderate complexity (6 params, output schema exists), the description covers purpose, usage, parameters, and best practices comprehensively. No missing context.

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?

With 0% schema coverage, the description must explain parameters. It covers from_dt, to_dt, fields, limit, and summary well (types, defaults, behavior). However, the required parameter 'mint_id' is not directly explained beyond 'identified machine', leaving ambiguity.

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 retrieves operational history for a machine, with each row being canonical output from normalize calls. It distinguishes from siblings like normalize_telemetry and get_coverage by focusing on historical queries.

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' section lists concrete scenarios (utilization, health trends, period comparisons) and advises starting with summary=true. This provides excellent guidance on when and how to use the tool.

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