Tideline Money Tools

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds context about 'honest burn including paying yourself' and the two runway types, which explains the calculation's intent. However, it doesn't mention output format, assumptions, or any edge cases (e.g., negative cash, missing income), leaving gaps for an agent.

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, information-dense sentence that front-loads the core purpose and includes the key distinction between hard and working runway. There is no filler or 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?

The tool has five parameters, no output schema, and no annotations, so the description must explain the output and how to interpret results. It only explains the high-level concept but doesn't state that the output is likely a number of months (or two numbers), nor does it mention required parameters or input validation. This is adequate but not complete for a tool of this complexity.

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 low (20%), so the description must compensate. It indirectly references parameters: 'paying yourself' maps to owner_draw, 'committed income' to monthly_income_committed, and 'honest burn' to monthly_expenses. This adds meaning, but not every parameter is explicitly tied, and the description doesn't clarify how required vs optional fields interact.

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 months of cash runway for a household or freelance practice, and distinguishes between hard and working runway. It implies a calculation and is distinct from sibling tools like debt_avalanche_plan or subscription_audit. However, it doesn't use an explicit verb like 'calculate' or 'compute', so it's slightly less direct.

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 specifies the target audience ('household or freelance practice') and explains the concept of hard vs working runway, giving some context on when it would be relevant. But it doesn't explicitly state when to use it versus alternatives, nor any exclusions or prerequisites.

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 transparency burden. It adds genuinely useful behavioral detail: the computation is local, deterministic, and month-by-month, with a comparison to minimum-only payments. It does not mention error cases (e.g., invalid APR, zero balance) but for a pure calculation this is a solid disclosure.

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 entire tool is described in one dense sentence with no wasted words: the topic, method, main outputs, and one notable guarantee ('All math local and deterministic') all earn their place. No redundant grammar or repetition; front-loading is strong.

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 calculation tool that yields no output schema, the description names the key outputs (debt-free date, interest comparison, extra $50 monthly effect) and the calculation style. It omits the exact output formatting and required-field validation details, but given the tool's simplicity and the if-schema available for input structure, an agent can call it without needing to know much else.

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 only 50%: only apr and extra_monthly have descriptions. The description helps by anchoring APR to the ordering rule ('highest APR first') and by referring to 'minimums' and the extra $50/month, which maps to the debts.minimum and extra_monthly fields. Yet it doesn't clarify balance semantics or add anything beyond the schema for fields like minimum, so it only partially compensates for the coverage gap.

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 'Debt payoff plan' and immediately names the exact behavior: avalanche ordering by highest APR, month-by-month simulation, and the key outputs (debt-free date, interest vs minimums, extra $50/mo effect). This distinguishes it from the sibling financial tools like real cash_runway and subscription_audit without needing to open the 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?

It makes explicit what the tool computes, so a user asking for an avalanche payoff plan or prepayment scenario can easily infer this is the right path. However, it never names alternatives or adds a when-not-to-use clause, so the agent gets no guidance for choosing among the sibling calculators or counterpart avoidance.

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 full weight. It openly discloses a non-obvious behavior: the denominator includes ALL hours logged, including unbilled time. It could also mention output shape or handling of missing client matches, but the core behavior is well conveyed.

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, front-loaded formula, and no filler. The 'Exposes which clients quietly pay half your rate' line adds practical value without bloat.

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?

The description covers the inputs, the calculation, and the intended decision. Since there is no output schema, it could explicitly state the return format, but the output concept ('real $/hr per client') is already 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?

Schema description coverage is 0%, so the description must compensate. It maps 'collected' to 'dollars actually collected' and 'work_entries' to 'ALL hours logged', giving both required parameters real meaning. It doesn't detail the inner object fields, but the schema already exposes client/amount/hours.

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 clear calculation: 'dollars actually collected divided by ALL hours logged' per client. It names the resource and purpose precisely and is easily distinguished from sibling finance tools like cash_runway or invoice_aging.

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 context: freelancers wanting per-client effective hourly rates to identify underpriced clients. It does not explicitly name alternatives or exclusion conditions, so it stops 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?

With no annotations, the description carries the full burden of behavioral disclosure. It conveys the core computation (aging buckets and chase action tiers), but it does not explicitly clarify that this is a read-only planning tool rather than an execution tool that sends chase actions. No output format or side-effect details are 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 a single, well-structured sentence with no filler. The core idea (invoice aging buckets) is front-loaded, and the action tiers add useful context without bloat.

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 two-parameter analysis tool, the description adequately explains the intent and inputs. However, with no output schema and no annotations, it should also clarify what the tool returns, such as bucket labels and action recommendations, which are only vaguely referenced by 'aging buckets' and 'chase action'.

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 50% because only 'today' is described. The description adds the useful notion of lateness-driven bucketing, but it does not explain how the invoice fields (amount, due, paid, client) factor into the calculation or how 'today' anchors the aging. It partially compensates for the schema gap without fully resolving it.

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's function: grouping invoices into aging buckets and assigning a chase action based on lateness. It is distinct from siblings like cash_runway and subscription_audit because it centers on invoice-level receivables follow-up, though it doesn't name those alternatives.

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 context is implied rather than explicit: use it when invoices need to be bucketed by age and matched to a follow-up action. It does not state when to prefer this over sibling tools or mention any exclusions, leaving some inference to the agent.

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, and it does so thoroughly. It discloses deterministic arithmetic, no market-data dependency, the OCC $0.01 rule, account-type restrictions, margin requirements, trap-zone behavior, and pin risk, giving an agent a strong behavioral model before invocation.

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 dense but every clause earns its place, front-loading the core purpose before moving into details. It is appropriately sized for an 11-parameter tool with no output schema, and it avoids repeating the schema.

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 tool with no output schema, the description covers most needed context: inputs, key rules, account constraints, risk scenarios, and deterministic behavior. The main gap is that it does not explicitly describe the return shape or how edge cases like OTM outcomes are presented, but the described scope is otherwise strong.

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 82%, so the baseline is 3. The description adds meaningful semantics beyond the schema by explaining account-type restrictions (margin, cash, IRA), the expected-move test, and the OCC auto-exercise threshold, which helps the agent reason about parameter choices without reading the schema alone.

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 explains what the tool does: it models what happens when a single-leg US equity option is in the money at expiration, covering auto-exercise, resulting share positions, account constraints, P&L, break-even, trap zone, and pin risk. It is specific about the resource (option expiry outcomes) and distinguishes itself from the unrelated financial-planning sibling 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 description makes the intended use clear: test expiration outcomes for a single-leg US equity option and optionally add an expected move. It does not explicitly name alternatives or say when not to use it, but the sibling tools are unrelated and the usage context is unmistakable.

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 description discloses useful behavior: it finds recurring charges and ranks them by annual cost, and it indicates that output quality depends on having 90+ days of history. But with no annotations provided, it leaves gaps about exact return structure, read-only expectations, and edge cases like missing dates, so the description carries only part of the behavioral burden.

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 with no filler: the first states the tool's purpose and behavior, the second gives the most important invocation guidance. Every clause earns its place, and the key purpose is 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?

For a one-parameter tool, the description covers the required input shape, the best-practice data window, and the output ordering. It does not describe the full response object or explicitly address sibling alternatives, but it is largely sufficient for an agent to prepare and call the tool.

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. It does by prescribing row contents as '(date, amount, description)' and recommending '90+ days.' It adds meaning beyond the bare schema, although it does not resolve the schema's optional date or specify date/amount formats.

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 a specific verb and resource: 'Find recurring charges in a transaction list.' It further clarifies scope ('subscriptions, price-similar repeats') and output ordering ('ranked by annual cost'), which cleanly separates it from sibling tools like cash_runway or invoice_aging.

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 context and an actionable data-preparation guideline: 'Send 90+ days of (date, amount, description) rows for best results.' However, it does not explicitly mention when not to use this tool or name alternatives, so it stops 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.