analyze_ai_spend

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

With no annotations supplied, the description carries the full burden and does real work: it discloses the input contract ('a local FOCUS 1.x CSV export'), a privacy guarantee ('nothing is sent anywhere'), and the full set of computed results. It does not say what happens with a malformed or non-FOCUS file, whether the weekly price archive requires network access, or how expensive the scan is.

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?

Content is front-loaded: the identity of the tool comes first, then capabilities, then selecting questions. The main body is one very long compound sentence that is information-dense with no filler, though breaking the enumerated outputs into a list would scan better.

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 two-parameter tool with no output schema and no annotations, the description compensates well by spelling out the return content in detail and naming the required input format and privacy posture. The remaining gap is failure modes and any dependency on the external weekly price archive.

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 100%, so both parameters are already documented, and the baseline is 3. The description adds meaning to file_path by specifying it must be a FOCUS 1.x export, but says nothing about top_n's truncation semantics or ordering on top of the schema's 'largest spend first'.

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 uses a specific verb and resource ('FinOps for AI on the user's actual bill') and enumerates the exact providers it covers and the exact outputs it computes (spend per model split by input/output/cache-read/cache-write, paid vs list, cross-provider comparison, cache savings, price changes). It never names a sibling tool, so differentiation from analyze_bill, compare_token_pricing, or llm_price_changes is left to inference, but the AI-spend scope is unambiguous.

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 closing 'Use for:' clause gives three concrete user questions that select this tool, which is strong positive guidance. It does not state when NOT to use it or point at the sibling to use instead (e.g., analyze_bill for non-AI spend), so it stops short of full routing guidance.

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