get_llm_costs

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

No annotations are provided, so the description fully carries the burden. It transparently describes the output: total spend, breakdowns, trends, and recommendations. It is clear that this is a read-only query tool with no side effects.

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 well-structured with a concise main paragraph, formatted Args section, and relevant examples. Every sentence adds value, and it is appropriately sized.

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?

With no output schema and no annotations, the description covers parameter details, usage context, and examples. It lacks explicit return type description, but for an aggregation tool this is acceptable.

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?

Despite 0% schema description coverage, the description includes an 'Args' section explaining each parameter: days (default 30, ignored if start_date set), start_date, end_date (defaults to today). This adds significant meaning beyond the raw schema.

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 that the tool aggregates AI/LLM spend across all configured providers, listing specific providers (OpenAI, Anthropic, etc.). It distinguishes itself from siblings like 'get_bedrock_costs' or 'get_llm_cost_by_model' by being a comprehensive aggregate view.

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?

Examples provide context for when to use, but there is no explicit guidance on when not to use or comparison to alternatives. The description implies usage for overall spend analysis, but lacks exclusions.

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