Earnings quality and financial statement risk benchmarks — accruals ratio, cash conversion, and revenue recognition risk by sector. Source: SEC EDGAR aggregate + Sloan accruals model (academic standard). For CFOs, auditors, and analysts assessing financial reporting risk before M&A or investment.
| Name | Required | Description | Default |
|---|---|---|---|
| sector | Yes | ||
| revenue_recognition_model | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond this by specifying the data source (SEC EDGAR aggregate + Sloan accruals model) and the risk components included. It does not contradict annotations and enhances understanding of the tool's content.
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: two sentences that front-load the core purpose, then add source and target audience. Every sentence earns its place with no redundancy.
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?
The description explains the purpose and context well but lacks details on return values (no output schema) and does not describe the 'revenue_recognition_model' parameter. For a moderate-complexity benchmark tool, it is adequate but leaves key usage details unaddressed.
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 description coverage is 0%, but the description mentions 'by sector', which aligns with the required 'sector' parameter. However, it does not explain the 'revenue_recognition_model' parameter at all, leaving its meaning and usage unclear despite the schema enums. Partial compensation for one parameter only.
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 resource: 'Earnings quality and financial statement risk benchmarks' with specific components (accruals ratio, cash conversion, revenue recognition risk) and sector focus. It distinguishes from sibling tools by emphasizing financial reporting risk benchmarks, though it lacks an explicit verb like 'retrieves' or 'lists'.
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 provides clear context on when to use this tool: 'For CFOs, auditors, and analysts assessing financial reporting risk before M&A or investment.' It does not explicitly mention alternatives or when not to use it, but the target audience and scenarios are well defined.
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.
Multiple tools have overlapping purposes, e.g., get_inflation_benchmark and get_bls_inflation_components, get_commodity_benchmark and get_agricultural_commodity_benchmark. Descriptions provide some differentiation, but many benchmark tools cover similar domains, leading to high potential for misselection.
All tools follow a consistent 'get_' prefix with snake_case nouns, e.g., get_inflation_benchmark, get_ma_multiples_benchmark. No mixing of conventions or irregular naming patterns.
46 tools is excessive for a server focused on financial benchmarks and intelligence. While the domain is broad, many tools could be consolidated. The high count may overwhelm agents and suggests insufficient scoping.
The toolset covers a wide range of financial data—benchmarks, regulatory filings, commodity prices—but lacks granular tools like individual stock prices or sector-specific indices. Some areas (e.g., credit unions) are well-covered, but other common financial operations (e.g., portfolio analytics) are absent.