BEA personal saving rate from FRED with long-run average comparison and above-average flag. Use when assessing household balance sheet health, consumption durability, and macro recession risk. Source: FRED / BEA. $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 adds meaningful context: the data source (FRED/BEA), the computation (long-run average comparison), the cost ($0.02 atomic), and cryptographic attestation with verification URL. This goes beyond annotations, though it does not describe return format or time range.
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 compact and front-loaded: the first sentence states the core deliverable, the second states when to use it, and the next two provide sourcing, cost, and verification details. Every sentence adds distinct value 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 zero-parameter, read-only benchmark tool with good annotations, the description covers the key aspects: what is returned, the source, the use cases, and additional trust/cost details. It lacks explicit return format or units, but the complexity is low and the description is sufficiently complete for selection and 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 tool has zero parameters, so the description does not need to explain parameter semantics. The baseline of 4 applies because there is nothing to document and the description still clarifies what the returned metric represents.
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 what the tool provides: the BEA personal saving rate from FRED, along with a long-run average comparison and an above-average flag. This distinguishes it from sibling benchmark tools by naming the specific economic indicator and the value-added comparison.
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 gives explicit use cases ('Use when assessing household balance sheet health, consumption durability, and macro recession risk'), telling the agent when to select this tool. It does not mention exclusions or alternatives, but the context is clear and sufficient given the narrow tool scope.
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.