estimate_cost

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

With no annotations, the description carries full disclosure burden. It reveals non-obvious behavior: non-1:1 request ratio (specific counts for fmp_search, fmp_price_target, fmp_quote), cached legs cost nothing, worst-case estimation, and returns budget context. This goes beyond a basic purpose statement.

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 concise and well-structured, starting with the core purpose, followed by usage rationale, an example, and a summary of returns. Every sentence adds value, and the format makes it easy to parse.

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?

Despite a single parameter, the tool needs explanation of request ratios, caching, budget, and returns. The description covers all these aspects, and the presence of an output schema reduces the need to describe return format in detail. It's complete for the tool's purpose and usefulness.

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?

The schema provides no field descriptions (0% coverage). The description compensates fully with a precise example of the 'calls' array, including the tool and args structure, making the parameter semantics unambiguous.

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: 'Say what a set of tool calls will cost upstream before running them.' This clearly distinguishes it from data-fetching siblings like fmp_profile or fmp_quote, as it is a cost-estimation meta-tool.

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?

It gives explicit context: 'Use this before a multi-step analysis, or whenever a plan touches many symbols.' It also explains caching and request ratios, providing rationale. However, it doesn't explicitly state when not to use it or alternatives, just a clear 'when to use'.

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