Insurance financial performance benchmarks — combined ratio, loss ratio, expense ratio, and reserve adequacy by line of business. Source: NAIC annual statistical report. For insurance CFOs, actuaries, and analysts reviewing underwriting performance.
| Name | Required | Description | Default |
|---|---|---|---|
| company_size | No | ||
| line_of_business | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read operation. The description adds the data source (NAIC annual statistical report) and metric scope, but does not disclose other behaviors like output format or pagination. This is acceptable given annotation coverage, so a neutral score of 3.
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 three short, information-dense segments, front-loaded with the core purpose. It avoids redundancy and every sentence adds context (scope, source, audience).
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 simple read-only benchmark tool with only two parameters and no output schema, the description covers the essential aspects: what (metrics), how (by line of business), where (NAIC source), and who (insurance professionals). It doesn't describe the exact return structure, but that's less critical with no output schema and a clear data product.
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 has zero description coverage, so the description carries the burden. It explains that benchmarks are broken down by line_of_business and lists the metrics returned, but it does not describe the company_size parameter at all. The enum values are self-explanatory, but a full parameter description is missing.
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 identifies the tool as providing insurance financial performance benchmarks, enumerates the specific metrics (combined ratio, loss ratio, expense ratio, reserve adequacy), and distinguishes it from sibling industry benchmarks by specifying the insurance domain. While it lacks a verb, the tool name 'get' plus the description makes the purpose unambiguous.
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 states the target audience and use case ('For insurance CFOs, actuaries, and analysts reviewing underwriting performance'), giving clear context. It does not explicitly name alternatives or exclusions, but the insurance-specific framing implicitly differentiates it from sibling benchmark tools.
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.