Baltic Dry Index shipping cost benchmark from FRED with trend classification (elevated, depressed, normal) and five-year average. Use when assessing global trade volume, freight inflation, or supply chain cost pressure. Source: FRED / Baltic Exchange. $0.02 atomic. 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 read-only and non-destructive behavior, so the description adds valuable extras: the $0.02 atomic cost, post-quantum signed settlement receipt, and verification URL. These disclose financial and cryptographic traits beyond the annotations, which is useful for an agent.
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 front-loaded with the core purpose and usage, followed by source, cost, and attestation details. It is not overly verbose, though it could be slightly tighter without losing information.
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 tool with no input parameters and no output schema, the description is fully complete: it explains what data is provided, the trend classification, the five-year average, the source, cost, and verification process. Nothing essential is missing.
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?
There are zero parameters, so the schema needs no additional explanation. The description does not need to add parameter details. The baseline for 0 params is 4, and the description appropriately omits irrelevant parameter info.
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 the Baltic Dry Index shipping cost benchmark from FRED with trend classification and a five-year average. This is a specific verb+resource combination, and it distinguishes itself from sibling benchmark tools by naming a unique index and source.
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 explicitly says to use when assessing global trade volume, freight inflation, or supply chain cost pressure. This provides clear context for when to use the tool, though it does not explicitly mention alternative tools or when not to use it, which is acceptable given the specificity.
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.