compoundsim

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

Annotations already cover safety (readOnly, idempotent, closed-world), so the description's job is to add what they can't: it discloses that results come from the same simulation engine as simulate_portfolio and enumerates the returned fields, which matters because there is no output schema. It doesn't mention cost, latency, or how ties/degenerate portfolios are handled, keeping it 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?

One sentence, verb-first, with the engine equivalence and the returned fields packed into a single readable clause. No sentence restates the title or the schema, and the example question is placed last where it clarifies rather than delays.

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 10-parameter comparative simulation with no output schema, the description supplies the missing return-shape information and the simulation semantics, which is the main gap that structured fields leave. Currency and inflation effects are deferred entirely to schema descriptions, which is acceptable given 100% coverage.

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 schema fully documents parameters including asset tickers, presets, and the history/inflation knobs; baseline is 3. The description adds only the 2–4 portfolio bound (already in minItems/maxItems) and that monthly investing is applied uniformly across candidates.

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 ('Runs the same monthly-investing plan through 2 to 4 portfolios') and enumerates the outputs (final outcome range, deepest fall, past return, volatility, url). It explicitly anchors itself to a sibling ('the same simulation as simulate_portfolio'), so an agent can place it among the simulate_* family without opening schemas.

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?

Gives a concrete triggering scenario ('S&P 500 or all-country stocks?') and states the cardinality constraint (2 to 4 portfolios), which implicitly routes single-portfolio questions to simulate_portfolio. It never explicitly says when NOT to use it or names the single-portfolio alternative as the fallback, so the routing is inferable rather than 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?

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds genuine context by naming the computed metrics and the dividend-reinvestment convention, but omits what the response looks like and how non-USD currency figures are derived in practice, stopping short of rich behavioral 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?

It is a single ~55-word run-on sentence that bundles purpose, periods, metrics and currency semantics together, with compressed fragments like 'compoundsim simulates' and 'the dividends' share'. The size is reasonable and the subject is front-loaded, but the lack of sentence breaks hurts scannability.

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 usefully enumerates the metrics returned, and all six parameters are documented in the schema. What is missing is any note on the response structure or units beyond the currency remark, so it is strong but not fully self-contained for a data-returning 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 coverage is 100%, so the baseline is 3, but the description earns more: it explains that '5, 10 or 20 years, all the data, or a calendar span' map to period and the period_from/period_to pair, and it clarifies the currency semantics (USD inflation adjustment versus exchange-rate share in another currency) beyond what the enums convey.

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?

It states a specific resource and computation ('annualised past returns of the assets') and enumerates the metrics returned (total return, price return, dividend share, volatility, inflation-adjusted return), which clearly separates it from the simulate_* siblings that project forward. The phrasing 'the assets compoundsim simulates' is awkward and slightly circular, but the purpose is still 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?

There is no explicit statement of when to use this instead of compare_portfolios or simulate_portfolio, and no prerequisites or exclusions. Use is only implied by the word 'past', leaving the agent to infer that this is the historical-lookup tool rather than a forward simulation.

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 the safety profile is covered; the description adds the important trait that the simulation is deterministic rather than stochastic. It also previews the shape of the output (contributions, gains, total by year, plus a compoundsim.app URL), which the absence of an output schema makes valuable.

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 zero waste; the core operation is front-loaded and the disambiguation follows immediately. Nothing in it 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?

For a 7-parameter read-only calculator with full schema coverage and no output schema, the description supplies both the output shape and the deterministic-versus-stochastic distinction an agent needs. Only minor gaps remain, such as how inflation-adjusted outputs are presented while the schema already covers it.

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 every parameter, including the currency and language enums and the inflation semantics, is already documented in the schema. The description only echoes 'monthly contributions (plus an optional starting sum)' and does not add syntax or interaction details beyond the schema 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?

States a specific verb and resource ('deteministic compound growth of monthly contributions... at a fixed annual return') and names the sibling it is not. The contrast with simulate_portfolio is stated in the description itself, so the agent can separate the two without opening schemas.

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 simulate_portfolio 'for the range of outcomes of real assets,' which is the key alternative behaviour an agent would confuse this with. It gives a clear positive use context (fixed-rate, deterministic projections) but no explicit when-not or prerequisite notes.

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 the safety profile is covered. The description does add useful output-behavior context (percentile band by year, deepest drawdown, the mix's historical return/volatility, and a compoundsim.app link reproducing the scenario), but says nothing about cost, expected runtime, or whether the simulation is stochastic/non-deterministic between calls — a notable omission for a Monte Carlo 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?

A single sentence, front-loaded with the method and scenario, then the return payload, then the link. It is long but every clause carries information; only the URL clause is arguably optional.

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 correctly takes on the burden of describing returns (percentiles by year, max drawdown, historical stats, shareable URL), which is exactly what an agent needs to report results. It omits assumptions that matter for interpretation, such as how non-USD currency paths are modeled, though the schema covers that in the currency field.

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 100% and every parameter carries a detailed description (asset ticker meanings, preset compositions, history spans, inflation semantics), so the schema does the heavy lifting. The prose only echoes the optional starting sum and the asset-vs-mix choice, adding no syntax or constraint detail beyond the schema; 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?

States a specific verb and method (Monte Carlo simulation) applied to a specific resource (monthly investing in one asset or a monthly-rebalanced mix), including the optional lump sum. It does not explicitly name or distinguish itself from siblings like compare_portfolios or simulate_withdrawal, but the monthly-accumulation framing is distinctive enough to separate it from a fixed-rate or withdrawal simulation.

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 scenario (accumulating via monthly contributions, with or without a starting sum) but gives no explicit when-to-use / when-not-to-use guidance and never names an alternative. An agent must infer that compare_portfolios is the choice for comparing multiple mixes rather than simulating one.

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 cover the safety profile (readOnly, idempotent, closed-world), and the description goes further by enumerating exactly what comes back — success share, exhaustion timing, balance range in today's money, a 2%-8% rate sweep — which is real added context. It stops short of noting simulation cost/runtime or how many paths are run.

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 core action is front-loaded in the first clause and the return listing follows in one sentence with no filler. The output enumeration is long but each item is a distinct return value an agent would want.

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 10 parameters and no output schema, the description carries the burden of describing returns and does so thoroughly, so an agent knows what to expect. Remaining gaps — simulation size, runtime, determinism beyond idempotentHint — are minor for a read-only calculation 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 coverage is 100%, so every parameter is already documented. The description adds only framing, that the withdrawal rises with inflation monthly and results are in today's money, which sharpens withdrawal_rate_pct and inflation_percent but does not explain the history window or portfolio presets beyond 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 and resource (Monte Carlo simulation of withdrawing a yearly percentage from a portfolio) plus the method and the 4% rule framing, which clearly separates it from a growth-only or fixed-rate simulation. It never names the sibling tools, so the differentiation is inferable 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?

The retirement drawdown scenario is implied by the title and by the 'yearly percentage of a starting balance, raised with inflation' mechanic, so an agent can infer when to pick this over simulate_fixed_rate. But there is no explicit when-to-use statement, no exclusions, and no mention of the alternatives.

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