JOLTS labor market intelligence from BLS: job openings, quits rate, layoffs rate, and tight/loose/normal interpretation. Use for workforce planning, wage pressure forecasting, and recession early-warning agents. Source: BLS JOLTS. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context beyond this: source (BLS JOLTS), cost ($0.10), cryptographic attestation, and a verification URL. This is more than just restating safety hints and enriches the agent's understanding of reliability and cost.
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 sentences, each earning its place. The opening line immediately conveys the core purpose and metrics, followed by use cases and then source/cost/attestation details. It is front-loaded, jargon is meaningful, and there is no wasted text.
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 no parameters and no output schema, the description fully compensates by enumerating the data points included (job openings, quits rate, layoffs rate, interpretation), source, pricing, and verification steps. It gives an agent enough information to decide to call it and understand what it will receive.
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?
The tool has zero parameters, so schema coverage is effectively 100%. Per the rubric, the baseline for 0 params is 4. The description does not need to add parameter semantics since there are none, and it correctly focuses on the output content and use cases.
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 provides JOLTS labor market intelligence from BLS, listing specific metrics (job openings, quits rate, layoffs rate) and an interpretation. This distinguishes it from all sibling tools, none of which mention JOLTS, and gives a specific verb+resource (get intelligence).
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?
Explicitly states use cases: 'Use for workforce planning, wage pressure forecasting, and recession early-warning agents.' This provides clear context for when to invoke the tool, though it does not mention alternatives or when-not-to-use scenarios, which would make it a 5.
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.