Get Financial Metric

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

Beyond the annotations (read-only, idempotent), the description discloses important behaviors: automatic alias resolution, deduplication, and one-value-per-period output. This adds value beyond the structured hints.

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 front-loaded with the core purpose and then gives relevant details. The list of available concepts is long but directly useful for parameter selection. No redundant or filler sentences.

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 there is no output schema, the description describes the return shape (one-per-period values, trend analysis) but could be more explicit about the exact format. Still, the combination of annotations, schema, and description covers the essential usage context sufficiently.

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 input schema already covers all parameters (100% coverage), so the baseline is 3. The description enriches this by listing the available friendly names and explaining that raw XBRL tags are accepted, which goes beyond the schema's generic placeholder text.

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 historical values of a financial metric for a company, explicitly mentioning deduplication and alias resolution. This distinguishes it from siblings like get_financial_summary or analyze_financials, which have different scopes.

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 used when you need a single metric's historical time series with clean, deduplicated data. It does not explicitly compare with alternative tools, but the purpose is clear enough to infer appropriate usage.

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