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

With no annotations, the description fully carries the behavioral burden. It discloses that figures are dated observations, floors of located disclosure, never measures of activity, and that status stays pre_operational until conditions are met. It also notes append-only behavior with include_history and that record_id overrides view, adding significant context beyond what the schema provides.

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 dense but well-organized: it front-loads the core purpose, then lists return contents, then provides usage alternates, and finally explains filtering and behavioral caveats. It is longer than ideal, but every sentence adds necessary detail for a complex tool with 10 parameters; no filler is present.

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 the tool's complexity (10 parameters, no output schema), the description is thorough: it specifies return tables, filter semantics, behavioral nuances, and tier availability. It also covers all parameter semantics and provides examples. There are no significant gaps for an agent to call it correctly; even without an output schema, the return contents are enumerated.

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%, so baseline is 3. The description adds meaning by explaining the overall filtering logic (e.g., 'filter with ...' lists) and clarifies that `view` selects tables, `record_id` overrides `view` and includes line items, and `include_history` returns the append-only series. It also explains that quantities are published as-is and never summed, which gives context to the parameters, slightly exceeding baseline.

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 states a specific verb ('Retrieve') and resource ('Compute Commitment Register') with detailed scope: verified commitments, one record per instrument, never summed, and lists the exact tables and definitions returned. It clearly differentiates from siblings like get_event, query_scp, and get_metrics by explicitly stating what the register touches and what to use instead for those cases.

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 explicitly provides when to use this tool vs alternatives: 'for an attributed event use `get_event`; for sovereign capability use `query_scp`; for coordination metrics use `get_metrics`,' and clarifies that the register only touches events through Challenge Attributions. It also explains how filtering works and what the `view` parameter controls, giving clear guidance for common use cases.

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