record_project_business_outcome

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

The description adds important context beyond the annotations: the receipt classifies movement but does not claim causation, and it explicitly disclaims side effects like updating a skill, promoting a policy, or authorizing another action. It also clarifies the durable-event requirement for source_event_id, which an agent could not infer from the schema alone.

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 well-structured: purpose first, then the confirmation guardrail, then the semantic boundary. Every sentence adds distinct value, and there is no repetition of schema titles or obvious filler.

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?

It covers the confirmation gate, the source-event exception, and the receipt's semantic limits, which is strong for an append operation. However, it does not state what happens when confirm_record is false, how the receipt is returned, or how to obtain required identifiers, leaving some gaps for a tool with this many parameters and zero schema descriptions.

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 must carry parameter meaning, but it only elaborates confirm_record and source_event_id. Required fields such as direction, baseline_value, observed_value, metric_id, and metric_label rely on name inference, and optional JSON fields like links_json and evidence_refs_json receive no explanation. For a 14-parameter tool this is a significant gap.

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 opens with a specific verb ('Append') and a clear resource ('a real business metric observation'), plus a meaningful precondition ('after explicit confirmation'). The 'business metric observation' phrasing distinguishes this from sibling tools like record_project_science_evidence and list_project_business_outcomes, even without naming them.

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 tool gives an explicit confirmation condition: set confirm_record=true only when the user confirmed the numeric observation or when source_event_id points to the durable same-project event containing it. It does not name alternative tools or explicitly say when not to use it versus those alternatives, so it is clear but not fully comparative.

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