finnhub_basic_financialsKey financial metrics for a company — valuation, margins, growth, per-share and 52-week ratios. Finnhub REST: GET /stock/metric.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | No | Metric group to fetch — 'all' (default) returns every available metric. | |
| symbol | Yes | Stock ticker symbol, e.g. 'AAPL' (required). |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=true already signals a safe read operation, so the description does not need to repeat that. It adds useful context via the REST endpoint and the note that 'all' returns every metric, but does not disclose additional behaviors like rate limits or authentication needs. This is adequate for a simple GET.
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 one concise sentence followed by the REST endpoint. It is front-loaded with the core purpose and has zero filler. Every word earns its place.
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 two-parameter read-only tool with no output schema, the description provides a sufficient overview of what data is returned (valuation, margins, growth, etc.). It could mention return format, but the REST endpoint and metric categories give enough context for an agent to select and invoke the tool correctly.
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 coverage is 100%: both parameters (symbol and metric) are documented, including the default value for metric. The description mentions metric categories but does not go beyond schema details. Baseline 3 is appropriate since the schema carries the semantic weight.
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 provides 'key financial metrics for a company' and enumerates categories like 'valuation, margins, growth, per-share and 52-week ratios.' This is a specific verb-resource pairing that distinctly separates it from sibling tools like finnhub_quote (current price) or finnhub_company_profile (company details).
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 gives clear context for when to use the tool by listing the types of financial metrics it returns. It does not explicitly name alternatives or exclude other tools, but an agent can infer this is the go-to for fundamental financial ratios. The REST path adds further clarity.
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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Each tool targets a distinct resource or data type (company profile, news, earnings, insider transactions, quotes, etc.). The two news tools (company_news vs market_news) and the two earnings tools (calendar vs surprises) are clearly differentiated by description, leaving no ambiguity for an agent.
All tool names follow a consistent pattern: the `finnhub_` prefix followed by a lowercase snake_case noun phrase (e.g., `finnhub_company_news`, `finnhub_market_status`). This uniform naming convention makes tool selection predictable.
With 15 tools, the set is well-scoped for a financial data server, covering a broad range of endpoints without unnecessary bloat. Each tool has a clear purpose and corresponds to a meaningful Finnhub API endpoint.
The tool set covers core financial data needs: company profile, financials, news, earnings, quotes, recommendations, and market status. Minor gaps exist, such as lack of historical price candles or full financial statements, but these are not critical for the apparent server purpose and most workflows are supported.