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

With no annotations, the description must carry behavioral load, and it discloses the calculation model, the binding-constraint logic, and the fixed 3/5/10/20% down-payment scenarios. It does not state that the tool is a side-effect-free pure computation, but the methodology disclosure is substantive.

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?

Well front-loaded: the core purpose and governing rules lead, with mechanical Args/Returns structure after. The down-payment scenario detail is slightly extraneous but every part is readable and the purpose is not buried.

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?

An output schema exists, so return values need not be elaborated, and the description still summarizes them. Combined with the methodology disclosure and all four parameters explained, an agent has what it needs, though sibling differentiation and usage conditions remain thin.

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 carries the full burden, and it does compensate: it clarifies that income is gross and pre-tax, that debts are existing monthly obligations, and that the rate is a percentage and the term is in years. Units and intent are added beyond the bare schema titles, though no valid ranges or edge-case handling are given.

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 precise verb (calculate) and resource (maximum affordable mortgage) and names the governing methodology (standard 28% front-end / 43% back-end DTI). This clearly distinguishes it from siblings like calculate_monthly_payment or get_amortization_schedule, which do not perform affordability inversion.

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?

Usage is implied by the DTI-based affordability framing, but the description never states when to prefer this over siblings such as calculate_monthly_payment or compare_loan_scenarios, nor any prerequisites or exclusions. Adequate but leaves the routing decision to inference.

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?

No annotations are provided, so the description carries the full burden. It does disclose the return shape ('JSON object with the monthly_payment field') and the meaning of each input, but says nothing about validation boundaries (e.g., zero interest rate), rounding/precision, or error behavior for a pure-computation tool.

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 one-line purpose followed by compact Args/Returns blocks. Everything is readable and nothing is padded, though the Returns line duplicates information the output schema already carries.

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 three-parameter computation with an output schema present, the description covers purpose, all inputs, and the result key. The main remaining gap is the absence of any signal about how this base calculation relates to the six sibling tools.

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 0%, so the description must compensate, and it largely does: it documents all three parameters with units and worked examples ('Loan amount in dollars (e.g., 350000)', 'Annual interest rate as a percentage (e.g., 6.5)', 'Loan term in years (e.g., 30)'). It stops short of stating accepted ranges or constraints on those values.

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: 'Calculate the fixed monthly payment for a mortgage.' The scoping word 'fixed' distinguishes it from siblings like calculate_with_extra_payments and calculate_with_lump_sum, but the description never names those alternatives, so the differentiation is implicit rather than explicit.

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?

There is no when-to-use guidance at all. The description does not tell the agent when this base mortgage calculation is appropriate versus get_amortization_schedule, compare_loan_scenarios, or the extra-payment variants, leaving routing entirely to inference.

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?

No annotations are provided, so the description carries the full burden. It usefully discloses the computed outputs (break-even timeline, interest savings, recommendation), which signals a pure non-mutating calculation. However, it never states key behavioral assumptions that materially affect results, such as whether closing costs are paid upfront or financed, or how the new term extending the loan life is handled.

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 purpose sentence followed by compact Args/Returns blocks; every line carries information and nothing is padded. The Args list largely mirrors the schema parameter names, but the added units justify the space.

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 six required parameters, no annotations, and an output schema present, the description covers inputs, computation, and return shape well enough to call the tool correctly. The remaining gaps are modeling assumptions and edge-case behavior rather than anything that blocks 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?

Schema description coverage is 0%, so the description must compensate, and it does: all six parameters are documented with units and meaning ('annual rate as a percentage', 'in dollars', 'remaining_months' vs 'new_term_years' in years). The percent-vs-decimal distinction is especially valuable. It lacks any constraint notes (e.g., non-negative rates), keeping it from a 5.

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 opening sentence states a specific verb and resource ('Analyse whether refinancing a mortgage makes financial sense') and enumerates exactly what is computed: monthly savings, break-even month, and net interest impact. This is far more than a restatement of the name. It does not, however, explicitly distinguish itself from the overlapping sibling compare_loan_scenarios, so it stops short of a 5.

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?

Usage is implied by the topic (refinance analysis) but there is no explicit when-to-use/when-not-to-use guidance and no routing to alternatives. An agent must infer whether this or compare_loan_scenarios is appropriate for a given refinance question. Adequate but with a clear gap.

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?

With no annotations, the description carries the full burden. It does disclose the core behavioral mechanic (extra payment applied for a limited window, then reverting to base payment), which is genuinely useful. It does not, however, address edge cases such as extra payments exceeding the remaining balance or invalid input combinations, nor confirm the operation is a pure non-mutating calculation.

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 purpose sentence is front-loaded, followed by a well-organized Args block that maps one-to-one onto the parameters. There is little filler; the Returns line is arguably redundant given an output schema exists, but the overall structure is clean.

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 six-parameter calculation tool with no annotations but a present output schema, the description covers purpose, mechanism, and every parameter. Return-value explanation is correctly left to the output schema. Only the absence of any sibling-routing guidance keeps it from being fully 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 description coverage is 0%, so the description must compensate, and it does: all six parameters are documented with units and intent (dollars for principal and payments, percentage for the rate, months for the extra period). Minor gaps remain — no constraints on ranges or the relationship between monthly_payment and extra_monthly_payment.

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 ('Calculate accelerated payoff from extra monthly payments for a fixed period') and describes the mechanism, making the tool's purpose clear. However, it never distinguishes itself from closely related siblings like calculate_with_lump_sum or compare_loan_scenarios, so an agent must infer the boundary.

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 mechanism description ('extra amount is added to every payment for the first num_months_with_extra months, then reverts to base payment') implies the scenario this tool fits, but there is no explicit when-to-use guidance and no mention of the alternative tools for lump-sum or scenario comparison. Usage is inferable but not stated.

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?

With no annotations, the description carries the burden and does well: it explains that the lump sum reduces the outstanding balance at the specified month, that the same monthly payment is retained, and that the result is a shorter loan term. It does not cover edge cases (e.g., lump sum exceeding remaining balance, prepayment penalties, or how a partial final payment is handled).

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 purpose and mechanism before the Args/Returns listing; every line carries information. The docstring-style Args/Returns formatting is slightly verbose given an output schema already exists, but nothing is wasted.

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 6-required-parameter calculation tool with no annotations but with an output schema, the description covers the mechanics and every parameter. It omits error/edge-case behavior (invalid month, lump sum larger than balance), which is the only notable 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%, so the description must compensate, and it documents all six parameters with units and conventions: dollars, annual percentage, years, and notably that month_to_apply is 1-based. This is meaningfully more than the bare schema titles provide.

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 ('Calculate the amortization schedule') and scopes it to the one-time lump sum case, which implicitly separates it from calculate_with_extra_payments and get_amortization_schedule. It does not explicitly name the sibling it contrasts with, so it falls short of a 5.

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 scenario is described (one-time lump sum applied at a specified month), which implies when this tool applies, but there is no explicit when-to-use/when-not guidance or routing to calculate_with_extra_payments for recurring overpayments. Usage must be inferred from the scenario text.

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?

No annotations are provided, so the description carries the full burden. It discloses the return payload (both schedules plus delta metrics), which is useful, but says nothing about whether it is a pure computation with no side effects, validation rules, or failure modes. Adequate but not rich.

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?

Purpose and use cases are front-loaded, which is good, but the 'Args' and 'Returns' sections largely restate the schema and output schema. The Args units are valuable, yet the Returns block duplicates what the output schema already declares.

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 8 required params, an output schema, and no annotations, the definition supplies units for every input and describes both the use cases and the return structure. An agent could call this correctly; only side-effect/permission context 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?

With schema coverage at 0% and 8 parameters, the schema provides only bare titles. The description compensates by documenting each parameter and, importantly, its unit (dollars, annual percentage, years) and the human-readable role of the labels, adding real meaning over the schema.

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+resource (compare two loan scenarios side by side) and specifies the output shape (schedules plus delta metrics). This clearly distinguishes it from single-scenario siblings like calculate_monthly_payment, though it never names those siblings 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?

The 'Typical uses' line gives concrete conditions: 15-year vs. 30-year, two rates on the same principal, different loan amounts. Clear context for when to reach for this tool, but no exclusions or explicit routing away from the single-scenario calculators.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It is a deterministic calculation with no side effects, so the risk is low, and it briefly notes the return shape ('payment_info and the full entries list'), but it never states that it is a pure read/compute operation or mentions assumptions like rounding or payment timing.

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 purpose in the first sentence, then compact Args/Returns blocks with no filler. The Args section is slightly redundant with the schema, but it is short and each line adds unit 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?

An output schema exists, so the return structure need not be spelled out (and the description only summarizes it). All three 0%-documented parameters get semantic definitions, leaving only the cross-tool routing guidance absent.

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 0% and the properties carry only bare titles, so the description must compensate – and it does, defining principal as dollars, the rate as a percentage, and the term in years. Those units materially change how an agent fills the arguments, though no bounds or edge-case rules (e.g. zero-rate, rounding) are given.

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 ('Get the complete month-by-month amortization schedule'), which is clearly distinguishable from siblings like calculate_monthly_payment (single payment) or compare_loan_scenarios. It does not name those siblings explicitly, so it stops short of a 5.

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?

There is no statement of when to use this tool versus calculate_monthly_payment, calculate_with_lump_sum, or calculate_with_extra_payments. The 'complete month-by-month' phrasing implies the distinction, but nothing tells the agent when this is the right pick or when it is not.

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