RateZip Bank Deposit and Mortgage Rates

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and non-destructive, so the bar is lower. The description adds valuable context: it states the tool is illustrative, will 'state every omission (taxes, penalties, rate changes, tiers, availability),' and includes defaults for the APY values. This goes beyond the annotations by explaining the nature of the output and its limitations, though it does not detail the exact return format or edge-case behavior.

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 two sentences long and immediately states the core purpose in the first sentence. The second sentence packs in defaults, omissions, and a disclaimer without redundancy. Every sentence contributes essential information, making it highly concise and front-loaded.

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 the tool is a simple calculation with no output schema, the description is fairly complete. It explains what is computed, the default rates, and that all omissions will be stated. It lacks an explicit mention of the output format (e.g., dollar amount vs. percentage), but given the name and the context of an 'earnings difference,' this is inferable. The absence of return-value docs is acceptable because the tool's purpose is clear.

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 already covers all parameters with descriptions, including defaults for current_apy_percent and comparison_apy_percent. The tool description mentions the defaults again but adds no new parameter-level semantics beyond what the schema provides. Baseline of 3 is appropriate because schema coverage is 100%.

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 computes an 'illustrative annualized earnings difference between two savings APYs for a given balance and horizon,' which is a specific calculation task. It distinguishes from sibling tools (get_cd_rates, get_mortgage_rates, get_savings_rates) by focusing on comparing APYs rather than simply fetching rates. The title and name align, and the description adds value by specifying defaults and the illustrative nature.

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 implies the use case: when you have a balance and horizon and want to compare two savings APYs. It includes an explicit exclusion: 'never use the result to recommend an investment allocation' and notes 'not financial advice.' However, it does not name alternative tools or explicitly contrast with sibling rate-lookup tools, so it stops short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish read-only and non-destructive behavior, and the description adds substantial context: the composition of the data (FDIC averages plus observed rates), the presence of staleness status, and explicit behavioral rules (never recommend a provider, decline stock-moving advice). This goes far beyond what annotations state and gives the agent clear operational boundaries.

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 a single paragraph with four sentences, covering purpose, parameter usage, and behavioral guardrails. It is front-loaded with the core output and avoids redundancy, though it could be slightly tighter by trimming the example term list (e.g., '3, 6, 12, 24, 36, 60') which is not essential.

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?

There is no output schema, but the description compensates by detailing what data will be returned (terms, minimums/conditions, timestamps, sources, staleness status). It also covers edge cases around advice and provider claims. Given the tool's moderate complexity and full schema coverage, the description is sufficiently complete for safe and correct 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 100% description coverage, with each parameter already explaining its purpose (e.g., state adds availability note, balance resolves tier APY). The description only reiterates the term_months filtering behavior, which is already in the schema. No new parameter meaning is added, so the baseline 3 applies.

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 current CD APYs, specifically FDIC national averages plus RateZip-observed institution rates. It distinguishes itself from sibling tools by emphasizing 'CD' and the specific data sources, making it unambiguous what resource is being queried.

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 gives clear context for when to use the tool (for CD rate lookups) and how to filter by term_months. It does not explicitly name alternatives like get_savings_rates or get_mortgage_rates, so it lacks explicit exclusions, but the focus on CDs and the behavioral guardrails around financial advice imply the intended use case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses behavioral details beyond annotations: the data source (lenders' published rate pages), that no third-party national-average benchmark is redistributed, the presence of observation timestamp and staleness status, and the restriction to factual data only. This is valuable context beyond the readOnlyHint/destructiveHint annotations provided.

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 yet information-dense, with the primary purpose stated upfront. Every sentence adds value: the data grouping, the loan types, the filtering instruction, the comparison constraint, and the explicit limitations. It is front-loaded with the most critical information and is appropriately sized for the tool's complexity.

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 output schema, the description explains the return structure: each rate includes note rate, APR, representative-scenario conditions, observation timestamp, source, and staleness status. It also explains the grouping behavior and the optional filtering by loan_type. This makes the tool's behavior fully predictable without additional schemas.

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 schema covers 100% of parameters with descriptive text, so the description does not need to repeat those details. It adds a small nuance about comparing only within the same loan type, but this overlaps with the schema's loan_type description. The parameter descriptions in the schema already provide adequate semantics, so the description provides minimal additional value beyond the baseline.

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 'Current mortgage rates grouped by loan type', providing a specific verb and resource. It distinguishes from sibling tools like get_cd_rates and get_savings_rates by focusing exclusively on mortgage rates. The mention of 'RateZip observed on lenders' own published rate pages' adds specificity beyond the title.

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?

Provides explicit usage instructions: 'Pass loan_type to filter to one; omit for all' and 'Compare only within the same loan type.' It clearly states what the tool does NOT do: 'never estimate or invite a personalized quote, imply approval, or recommend a lender.' This gives the agent clear when-to-use and when-not-to-use boundaries, even without naming siblings directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: it mentions each row carries an observation timestamp, source, and staleness status, and that balance 'resolves the applicable tier APY; nothing is fabricated.' It also discloses that it only makes factual comparisons and never recommends providers, which goes well beyond the basic safety hints.

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: first sentence states the core data and sources, second explains the personalization and transparency, third sets behavioral limits. It is front-loaded with the purpose and contains zero filler or repetition.

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 no output schema, the description explains what rows contain (timestamp, source, staleness), how each optional parameter affects results (balance resolves tier APY, credit score returns a note), and what the tool refuses to do. It is complete for a read-only data-lookup tool, covering both data content and edge cases around advice.

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?

Schema descriptions already cover all three parameters (state, balance, credit_score) with meaningful explanations, and schema coverage is 100%, so the baseline is 3. The description adds a concise summary that the parameters 'personalize the results transparently' and clarifies that credit_score returns a clarifying note only, reinforcing the schema's detail. This adds a bit of value beyond the schema, justifying a 4.

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 opens with 'Current savings APYs: FDIC national average plus rates RateZip observed on institutions' own published rate pages,' clearly specifying the resource (savings APYs) and the scope (FDIC average + observed rates). It distinguishes itself from sibling tools like get_cd_rates and get_mortgage_rates by focusing solely on savings accounts, making the purpose 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 explains that optional parameters personalize results and explicitly states 'never recommend a provider or claim that RateZip can open an account,' setting clear boundaries for appropriate use. It also says 'decline financial advice without giving allocation or time-horizon rules,' which indicates when not to provide guidance. However, it does not explicitly compare to sibling tools or state 'use this tool when the user asks about savings rates' versus alternatives, so it's slightly shy of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.