Use when benchmarking working capital efficiency or preparing a CFO cash management brief. Working capital benchmarks — DSO, DPO, DIO, and cash conversion cycle (CCC) by industry and company size. Source: Hackett Group annual survey and BLS composite. CFO and treasury benchmark for lender covenant prep and cash flow optimization.
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | ||
| company_size | 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 that data comes from Hackett Group annual survey and BLS composite, and positions it as a CFO/treasury benchmark. This provides source credibility and context beyond the safety annotations, though it doesn't describe exact return format or pagination.
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?
Three concise sentences, front-loaded with the primary use case. Every sentence adds value: usage, metrics, and source. No redundancy or filler.
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 no output schema, the description is quite complete. It covers what the tool does, what metrics are included, the data source, and primary use cases. It doesn't detail return formatting but that's likely straightforward given the metric list.
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?
With 0% schema description coverage, the description compensates by explaining the tool breaks down benchmarks 'by industry and company size', which maps directly to the two parameters. This gives semantic meaning to industry and company_size even though enum values are only in the schema.
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 benchmarks working capital efficiency and lists the specific metrics (DSO, DPO, DIO, CCC) by industry and company size. The verb 'benchmarking' and resource 'working capital' distinguish it from sibling benchmark tools.
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?
It provides explicit use cases: 'Use when benchmarking working capital efficiency or preparing a CFO cash management brief.' It also names contexts like lender covenant prep and cash flow optimization. However, it doesn't explicitly exclude alternatives or name sibling tools, so it's clear but not fully explicit about when-not-to-use.
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.