fundamentals

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

With no annotations, the description bears the full burden of disclosing behavior. It only repeats the general purpose and parameter meanings, without explaining what is returned, how data is aggregated, or any limitations. For a data retrieval tool, this lacks important context like output format or coverage.

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—one sentence plus a short arg list. It is front-loaded with the core purpose. The arg list mirrors the schema, creating slight redundancy, but overall it remains efficient and scannable.

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?

Given the simple two-parameter design, the description provides adequate high-level context. But with no output schema, it does not explain the return structure or how the data_type options differ in output. This is a moderate gap for a tool that surfaces financial metrics and reports.

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 has 0% coverage, so the description must compensate. It does clarify that symbol is a ticker symbol and enumerates the allowed values for data_type ('all', 'info', 'financials', 'earnings'), which adds value beyond the bare schema. However, it stops short of describing what each data_type actually contains.

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 fetches fundamental financial data including metrics, financials, and earnings. This is a specific verb+resource+scope, and it distinguishes itself from sibling tools focused on price, technical indicators, or news sentiment.

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 implies the tool is for fundamental data queries, and the data_type parameter suggests different use cases (info, financials, earnings). However, it does not explicitly state when to use this tool versus alternatives like technical_indicators or earnings_calendar, nor does it mention any exclusions.

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