get_langfuse_model_costs

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

Discloses that it is a read-only operation returning cost and token metrics per model. Mentions required environment variables and optional host. Does not mention rate limits or error handling, but these are minor gaps given the simple read nature.

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?

Concise and well-structured: opens with a clear main purpose, followed by a brief detail, then requirements, args, return, and examples. Every sentence adds value, with no redundancy.

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?

Completely covers all necessary information: inputs (3 params), outputs (cost per model, tokens, efficiency), prerequisites, and usage examples. Given no output schema or enums, the description is sufficient for correct usage.

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?

Adds significant meaning beyond the input schema: explains 'days' as a lookback window with default and that it's ignored if start/end are provided, and describes start_date and end_date as ISO strings. This fully compensates for the 0% schema description 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?

Clearly states it breaks down LLM spend and token usage by model from Langfuse observability data. The verb 'break down' and resource 'LLM spend and token usage by model' are specific. It distinguishes from siblings like get_llm_cost_by_model by specifying the Langfuse data source.

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?

Provides prerequisites (API keys) and examples, but does not explicitly state when to use this tool versus alternatives like get_llm_cost_by_model or get_llm_costs. Implicitly it's for Langfuse users, but no explicit when-not or direct comparisons.

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