BLS Employment Cost Index year-over-year change for total compensation, wages, and benefits. Use when modeling labor cost inflation, contract escalation, and margin pressure in operating plans. Source: BLS ECI. $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 indicate readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: the data source (BLS ECI), the cost ($0.10 standard), and the cryptographic attestation and verification process. This goes beyond minimal safety disclosure and informs the agent of trust and contractual aspects.
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 sentences, each earning its place: the first states the data content, the second gives usage context, and the third provides provenance and verification. It is front-loaded with the core meaning, and there is no wasted verbiage.
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?
With zero parameters and no output schema, the description still tells the agent exactly what the tool returns (year-over-year change for three compensation components) and when to use it. It also covers source, cost, and verification, making it complete for an agent to invoke and trust the result.
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 there is no parameter burden to compensate. The schema coverage is 100% (empty schema), and the description explains what the retrieved data are, making parameter semantics unnecessary. Baseline for 0 params is 4, and the description meets that.
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 provides BLS Employment Cost Index year-over-year change for total compensation, wages, and benefits. It uses a specific noun phrase that identifies the exact data product, and the specificity distinguishes it from sibling tools like inflation benchmark or labor market benchmark.
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 explicitly says 'Use when modeling labor cost inflation, contract escalation, and margin pressure in operating plans,' giving clear use cases. It does not explicitly mention when not to use or alternatives, but the provided context is strong and sufficient for an agent to select this tool appropriately.
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.