get_llm_commitment_analysis

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

With no annotations, the description fully bears the burden. It discloses read-only behavior, configuration privacy (terms stay locally), and that it compares usage against contracts. Missing details on side effects or rate limits are acceptable for a read-only analysis tool.

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 sections for purpose, details, configuration, args, and examples. It is slightly verbose but every paragraph adds value. Could be tightened slightly.

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 tool with one parameter and no output schema, the description covers core functionality, configuration, and example outputs (coverage, utilization, effective rate). It is complete enough for an agent to understand and invoke the tool correctly.

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?

Although schema description coverage is 0%, the description explicitly defines the sole parameter 'days' with its default and purpose. This adds sufficient meaning beyond the schema, compensating for the low coverage.

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 the tool is for optimizing token spend against committed AI contracts, akin to reserved-instance analysis. It specifies supported providers (Azure OpenAI PTUs, AWS Bedrock, Anthropic) and what metrics it reports (coverage, utilization, effective rate, etc.), distinguishing it from generic cost tools and provider dashboards.

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 provides example questions the tool can answer and explains configuration via environment variable or JSON file. However, it does not explicitly state when to avoid using this tool or how it differs from similar tools like get_commitment_analysis or forecast_llm_costs.

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