get_fundamentalsNormalized income statement, balance sheet, and cash flow for a US-listed ticker, parsed from SEC EDGAR XBRL. Counts as one request on the Edgrapi plan.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of periods to return, 1-20 (default 5). | |
| period | No | Reporting period (default annual). | |
| ticker | Yes | US stock ticker, e.g. 'AAPL'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about the data source (SEC EDGAR XBRL) and billing plan, which are beneficial beyond the annotations. No contradictions.
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 sentences: first defines purpose and source, second adds billing info. No redundant phrases, perfectly front-loaded and efficient.
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 tool's simplicity (3 parameters, no output schema), the description is complete: it specifies the financial statements returned, data source, and billing cost. Could mention normalization briefly, but not necessary.
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%, so the schema already documents all parameters. The description adds no additional semantic meaning for the parameters beyond what's in the schema, which is adequate but not enhanced.
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 it retrieves 'income statement, balance sheet, and cash flow' for a US-listed ticker from SEC EDGAR XBRL. This distinguishes it from siblings like get_ratios or get_filings.
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 mentions the billing impact ('counts as one request'), but does not explicitly state when to use this tool versus alternatives like get_ratios or get_filings. Usage is implied by name and purpose but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct SEC EDGAR resource and action: company info, filings, fundamentals, ratios, insider trades, activist stakes, fund holdings, events, and narrative sections. There is no overlap in purpose, and descriptions clarify any potential ambiguity (e.g., get_activist vs get_holdings).
All tools follow a consistent 'get_' prefix followed by a singular or plural noun (e.g., get_company, get_filings, get_ratios). The pattern is uniform and predictable, making it easy to infer behavior from the name.
Nine tools is well-scoped for a financial data server covering company profiles, regulatory filings, and derived metrics. Each tool has a clear, non-redundant role, neither feeling sparse nor bloated.
The surface covers the core EDGAR resources: company metadata, filings, financial statements, ratios, insider activity, activist filings, fund holdings, material events, and narrative sections. Minor gaps exist (e.g., direct search for specific filing types or proxy statements), but agents can accomplish most research workflows without dead ends.