Use when a user needs to budget LLM observability tooling. Returns monthly cost at given request volume with retention adjustment.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | Yes | Observability provider | |
| avg_log_bytes | No | Avg payload bytes per traced request (default 4096) | |
| retention_days | No | Retention in days (default 30) | |
| requests_per_day | Yes | Average daily LLM requests |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the key behavior that retention affects the result and that output is a monthly cost, which is meaningful for a calculator. However it omits any mention of defaults (avg_log_bytes 4096, retention 30 days) or output structure, leaving gaps.
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 tight sentences with the usage condition front-loaded and the return behavior second. Nothing is wasted, though it is minimal rather than richly structured.
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 pure calculation tool with no annotations and no output schema, the description is adequate but thin. It tells the agent what is returned (monthly cost) but not the output shape (single figure vs breakdown) or that defaults apply, which would help callers interpret results.
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 the schema already documents all four parameters and their defaults. The description adds only the general notions of 'request volume' and 'retention adjustment', which map to but do not enrich the schema. Baseline 3 is appropriate.
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?
States a specific resource (LLM observability tooling cost) and an implied verb (compute monthly cost), which cleanly separates it from siblings like agent-loop-cost-calculator or eval-cost-calculator. It does not explicitly contrast itself with provider-cost-calculator, which could be confused since 'provider' here means observability vendor, keeping it short of a 5.
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?
"Use when a user needs to budget LLM observability tooling" gives a clear trigger condition, but names no alternatives or exclusions among the many sibling cost calculators. An agent must infer the routing itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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