MonitorBankRates

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful domain behavior: it specifies what each calculator returns, the rate assumptions, HELOC draw/repayment distinctions, and refinance breakeven output.

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 front-loaded with the estimate purpose and then enumerates modes efficiently. It is dense and delivered as one long sentence, but every clause carries useful mode-specific meaning.

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 an 11-parameter calculator with no output schema, the description covers the important assumptions, outputs, and mode-specific behavior. It could more fully specify return fields and edge cases, but the schema handles most parameter detail.

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 coverage is 91%, so the schema already documents most parameters well. The description still adds value by mapping parameters to calculator modes, such as mortgage accepting amount or home_price/down_payment, and refinance using current_rate for savings and breakeven.

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 states a specific verb and resource: calculating loan payment estimates, with five named calculator modes. It is clearly distinct from the sibling rate-lookup tools because it computes payments rather than listing rates.

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 clear usage context for each calculator mode and explains when to pass a rate versus using today's average. It does not explicitly name the sibling tools or say when not to use this tool, so it stops short of full alternative routing.

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?

The annotations already establish that this is a safe, read-only, idempotent operation. The description adds useful behavioral context by explaining the data source scope, daily computation, and the specific return contents including latest value, changes over 1 week / 1 month / 3 months, institution and quote counts, and daily history. It does not discuss authentication, rate limits, or pagination, which keeps it from a 5.

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 front-loaded with the core capability and return contents, then closes with practical example queries. Every sentence contributes useful information without 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?

Although there is no output schema, the description explains the return values in sufficient detail: latest rate, change over multiple time windows, institution and quote counts, and daily history. Combined with fully documented parameters and read-only annotations, an agent has enough context to invoke the tool correctly.

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 description coverage is 100%, so the input schema already documents all three parameters, including examples for keys and the national-default behavior for state. The description reinforces that the tool works at national or state level and mentions APY/APR distinctions, but it does not add parameter-level syntax or constraints beyond what the schema provides.

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 states a specific resource (national or state average rates), the metrics involved (APY for deposits, APR for loans), and how they are computed (daily from every published rate across 8,000+ banks and credit unions). This clearly distinguishes the tool from rate-search or best-rate siblings, even without naming them explicitly.

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 concrete example use cases such as 'what are CD rates doing', 'average mortgage rate in Texas', and 'are savings rates falling', which makes the intended context clear. It does not explicitly name when not to use it or point to sibling alternatives like get_best_rates, so it falls 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 declare read-only, idempotent, non-destructive, and closed-world, so the safety profile is covered; the description adds genuinely new behavior: one best quote per institution, health grade and as-of date included, and city mode putting local branches first with up to 3 addresses while filling from the state. It does not mention rate limits, caching, or how "live" rates are refreshed, so it stops short of a 5.

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?

Two dense sentences with the scoping and output shape front-loaded before the key-lookup pointer. Every clause carries information, though the long parenthetical product list is slightly heavy relative to its value.

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 takes on the burden of explaining return values and does so (one quote per institution, health grade, minimum deposit, as-of date, link, local-branch addresses). Combined with the schema for filters, an agent has everything needed to call it correctly.

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 coverage is 83%, so documented fields already carry the meaning of city, state, min_deposit, and product_key. The description restates the product_key sourcing (list_rate_keys) and product families but adds little beyond the schema, and it never mentions limit or institution_type, so the 3 baseline 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?

States a specific verb and resource ("Top live rates ... for one product") and enumerates exactly which rate categories are covered, plus the geographic scoping options (national/state/city). It also clarifies the output shape ("one best quote per institution"), which distinguishes it from the sibling get_average_rates without ambiguity.

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?

Explicitly routes the agent to list_rate_keys for valid product_key values and gives concrete key examples, plus clear rules for when to supply state, city, or omit both for national. It never states when to prefer a sibling like get_average_rates or get_mortgage_lenders instead, so the alternatives half of the guidance is missing.

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, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond the schema: hai is daily, hibi/aibi are annual, hai includes a 90-day history, and city input changes the shape of the result. It omits any error behavior (e.g. city without state) or rate-limit/availability info, keeping it from a 5.

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?

Front-loaded with the resource identity, then dense but information-bearing definitions; every clause about the index scale and returned fields earns its place. The long parenthetical for hai is harder to scan than a structured list would be, but nothing is filler.

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 three parameters, one required, and no output schema, the description covers what each call returns, the units and direction of each index, and the city/state behavior—enough to invoke correctly. Remaining gaps are behavioral edge cases (city specified without state, unknown state, granularity of national vs state results) rather than core semantics.

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 coverage is 67% and the 'index' enum has no inline description, but the description compensates fully by defining hai, hibi, and aibi and their scale direction ('higher is more affordable' vs 'higher is heavier'). The city/state relationship ('needs state') and the payoff of supplying a city are explained beyond the schema text.

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 names the resource (MonitorBankRates proprietary indexes) and defines each of the three enum values precisely, including what '100' means for each and what sub-scores come back. An agent can tell immediately this returns index data and nothing that any sibling (calculate_loan, get_average_rates, etc.) offers.

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?

Each index value is explained well enough that the agent can infer which to request, and the city/state interaction is described. However there is no explicit when-to-use versus when-not guidance, no mention of how this differs from get_average_rates or calculate_loan for affordability questions, and no stated prerequisites beyond one schema note.

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, idempotentHint, and openWorldHint=false, so safety is covered. The description adds meaningful context on the return payload (health grade, star rating, Texas ratio, branch states, page link), which matters because there is no output schema. No authentication or error behavior for an invalid slug is mentioned.

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?

Two dense sentences, zero filler, and the payload contents are front-loaded before the parameter instruction. Every clause earns its place.

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 one-parameter read tool with no output schema, the description is nearly complete: it enumerates returned fields and explains where the slug comes from. Only edge behavior (invalid/unknown slug) is left unaddressed, a minor gap.

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 coverage is 0% and the schema only declares `slug` as a bare string, so the description carries the load by explaining that the slug is the institution identifier obtained from search_institutions. That resolves the main ambiguity (format/provenance) an agent would hit.

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?

States a specific resource (rates + health metrics for one bank or credit union) and enumerates exactly what it returns: deposit and loan rates, health letter grade, star rating, Texas ratio, branch states, and page link. The 'one bank or credit union' scoping clearly separates it from get_average_rates, get_best_rates, and search_institutions.

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 instruction 'Pass the slug from search_institutions' establishes the prerequisite lookup flow and points at the correct sibling for discovery. It does not explicitly state when to prefer this over, say, get_average_rates, but the single-institution scope implies that unambiguously.

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, idempotentHint, and openWorldHint=false, so safety is covered; the description adds useful context beyond that: the HMDA data provenance, recency ('latest federal data'), and ranking methodology. It does not discuss result ordering ties or empty-result behavior, but against the annotation baseline this is solid.

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?

Front-loads the resource definition and ranking logic, then lists return fields, then closes with example queries. Slightly dense, but every clause carries information; the return-field enumeration is the only mildly padded portion.

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 does the work of enumerating return fields (loans closed, market share, approval share, median loan, programs, HQ, health grade), which is exactly what an agent needs. It stops short of describing result structure or response envelope, keeping it just under complete.

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 coverage is 80%, so the schema already documents nearly every parameter including the city fallback and lender_type semantics. The description restates the loan programs and lender categories but adds no format or edge-case detail beyond what the schema provides, so 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?

Names a specific resource (mortgage lenders and brokers) and scope (U.S. state or city), states the ranking basis (home loans closed in latest HMDA data) and the data source (MonitorBankRates directory). This is clearly distinguishable from siblings like get_institution and search_institutions, which are generic institution lookups.

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 concrete example queries ('best mortgage lenders in Florida', 'FHA lenders in Tampa', 'credit unions that do mortgages in Ohio') that map directly to the state/city/program/lender_type parameters, giving strong implied usage context. It does not explicitly say when to prefer this over siblings such as get_best_rates or search_institutions, so it falls short of a 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 declare readOnly, idempotent, non-destructive, closed-world; the description complements them by disclosing the actual payload contents (labels, product groups, APY/APR semantics, state codes). No permissions or rate-limit caveats are mentioned, but none are plausibly needed for a static lookup.

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?

Two sentences, zero filler, and the payoff (what a rate key carries) is front-loaded ahead of the secondary state-code behavior. Nothing could be removed 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?

With no output schema and no parameters, the description carries the full burden of conveying return values, and it does so concretely: key, label, product group, APY/APR flag, plus state codes. An agent can call this and interpret the result without further information.

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 takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The description correctly avoids inventing arguments.

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?

States a specific verb (lists) and resource (rate keys, U.S. state codes) and enumerates exactly what each entry contains: label, product group, and APY vs APR. It also names the sibling consumers (get_best_rates, get_average_rates), so it is unmistakable against the other tools.

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 phrase 'accepted by get_best_rates and get_average_rates' tells the agent this is the discovery step to run before those tools, which is a clear usage context. It stops short of an explicit instruction ('call this first') or exclusions, so it lands at 4 rather than 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 declare readOnly, idempotent, closed-world, non-destructive, so the safety profile is covered. The description adds value beyond that by disclosing directory size (8,000+), matching mode (by name), and the shape of each match including the slug handoff. Only pagination/limit behavior and no-match semantics are left out.

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?

Two sentences, zero filler: scope first, return shape second, with the get_institution handoff as the final clause. Nothing is repeated from the title or annotations.

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 two-parameter read tool with no output schema, the description covers what it returns, how it matches, and where the slug leads. The only real gap is result-count behavior, which matters given the 25-item maximum.

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 coverage is 0%, so the description must compensate. It constrains 'query' to name matching, but says nothing about 'limit' (default 10, max 25) or the two-character minimum, leaving an agent to discover the truncation behavior on its own.

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?

States a specific verb and resource ('Search ... directory of 8,000+ U.S. banks and credit unions by name') with explicit scope, and names the sibling get_institution as the follow-up. An agent can distinguish this from get_institution or get_mortgage_lenders without opening a schema.

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 name-based search scope and the 'slug to pass to get_institution' sentence make the intended workflow clear: use this to resolve a name, then fetch details elsewhere. It gives no explicit exclusions (e.g., when to prefer get_mortgage_lenders for lender lookups), so it stops short of full routing guidance.

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