analyze_cost_allocation

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

With no annotations, the description carries the burden well: it discloses that data is local ('a local FOCUS 1.x CSV export; nothing is sent anywhere'), which is meaningful privacy behavior, and the verb 'measures' implies a read-only operation. It does not cover failure modes (non-FOCUS or malformed CSV) or the shape of the result, but the analytical outputs are effectively enumerated.

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 sentences, front-loaded with what is computed and where the data comes from, followed by example triggers. Every clause adds information, and the parenthetical privacy note is short and load-bearing.

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?

For a 4-parameter analytical tool with no output schema, the description adequately conveys both inputs (local FOCUS CSV, tags) and the nature of the results. The one gap is that top_n's effect on output size is never explained, leaving an agent to infer it from 'biggest'.

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 75%, with file_path, tag_key, and required_tags already documented in the schema; the description reinforces this by mentioning 'tags used for showback / chargeback' and 'cost by the values of one tag (e.g. team)'. The undocumented parameter, top_n, is only hinted at through 'the biggest untagged services and resources', so no real added meaning there. Baseline 3 applies.

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 names a specific verb and resource ('FinOps allocation on the user's actual bill') and enumerates the concrete measurements produced: tag coverage, cost per tag value, cost by sub-account, and top untagged services/resources. This clearly separates it from generic siblings like analyze_bill or detect_bill_anomalies, which do not focus on tag allocation.

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 for:' list gives three concrete user questions that map directly to this tool, which is strong positive guidance. It stops short of explicitly naming the sibling to use instead when the user wants, say, anomaly detection or commitment analysis, so there is no stated when-not-to-use.

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