US advance retail sales from FRED in billions USD with month-over-month and year-over-year percent change. Use for consumer demand monitoring, recession signals, and revenue forecasting agents. Source: FRED / US Census Bureau. $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 declare readOnlyHint=true and destructiveHint=false, and the description complements this by adding source ('FRED / US Census Bureau'), cost ('$0.02 atomic'), and attestation details ('Cryptographically attested with a post-quantum signed settlement receipt'). It does not mention potential data revisions or update frequency, which could matter for advance estimates, but the added context goes beyond the annotations.
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 that front-load the core data content, then provide use cases, source, cost, and verification. Every sentence earns its place with no redundancy.
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 parameterless data-fetching tool, the description fully covers what data is returned, in what units, and for what purpose. The lack of an output schema is mitigated by specifying the data fields. The source and cost information further complete the context for invocation.
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 input schema has zero parameters with 100% coverage, so the baseline is 4. The description adds value by describing the output content (billions USD and MoM/YoY changes), which is relevant for interpreting results even without parameters.
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 returning 'US advance retail sales from FRED in billions USD with month-over-month and year-over-year percent change.' This specifies the exact resource, metric, and units, distinguishing it from sibling tools like get_consumer_sentiment_benchmark or get_bls_sector_employment. The verb is implied but 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 explicitly states 'Use for consumer demand monitoring, recession signals, and revenue forecasting agents,' providing clear use cases. It does not name alternatives or exclusions, but the specific topic makes it obvious when to select this tool over siblings. This is a minor gap compared to an explicit '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.