QuantCalc Retirement Engine

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

Annotations cover the safety profile (readOnlyHint, openWorldHint), and the description adds real context beyond them: the response reports success rate and median outcome per assumption set, omitted inputs fall back to a stated illustrative plan, and that plan is disclosed in the reply's first line. It still doesn't say whether any inputs override the illustrative plan wholesale or how many sources get compared by default.

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 with the operation and its distinguishing purpose, then the default-input behavior. No filler; every clause carries information an agent needs.

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 17-parameter, no-output-schema tool, the description covers the return shape (success rate and median per source) and the default-input path, which is what an agent needs to call it blind. Remaining gaps are minor: no note on runtime cost of running many simulations, and no cross-reference to run_retirement_projection as the single-source alternative.

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 adds meaning the schema doesn't: the no-required-argument calling pattern and the concrete illustrative plan (age 65, $1,000,000, $40,000/yr) that stands in for omitted inputs. It does not add anything about the sources or allocations parameters beyond what the schema already documents.

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 (runs) and resource (the same plan) plus the distinguishing scope: several published capital market assumption sets, returning success rate and median outcome under each. This is clearly separable from run_retirement_projection (single assumption set) and list_return_assumption_sources (just a listing) 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?

Usage is implied rather than stated: the 'how far the answer moves with the return forecast' framing tells the agent this is a sensitivity/comparison call, and the no-argument default path is described. However, it never says when to prefer this over run_retirement_projection or when a comparison is unnecessary, so the agent must infer the choice.

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 only declare readOnlyHint and openWorldHint; the description goes well beyond them by disclosing the licence gate and its failure mode, that the numbers come from the same engines the website's Return Preview is checked against to the cent, exactly what the result contains including assumed defaults and unmodelled items, and the 'estimate, not a filed return, not tax advice' limitation.

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 outputs are front-loaded, and the sentence chain is dense but each clause maps to real function (outputs, engine provenance, licence gate, disclaimer). The long parenthetical listing income types is partly redundant with the 41-parameter schema and could be trimmed without loss.

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 41-parameter tool with no output schema, the description compensates well: it enumerates the returned figures and Form 1040 lines, states that defaults and state-engine assumptions are reported, flags the unmodelled Marketplace premium tax credit reconciliation, and covers the licence prerequisite. Little an agent needs in order to call it correctly 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?

Schema description coverage is 100% across 41 parameters, so the schema already documents each field including defaults, phases-outs and constraints. The description's enumeration of income sources adds narrative framing but no syntax or edge-case detail beyond what the schema already carries, so the baseline of 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 (estimates) and resource (a 2026 federal and state income tax return) with the exact input categories and the exact outputs (federal/state refund or owed, main Form 1040 lines, defaults assumed, what is not modelled). It never names a sibling tool, so an agent must infer for itself why it would pick this over run_tax_aware_projection or explain_methodology.

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 name and by boundary statements ('An estimate, not a filed return and not tax advice'), and it does surface a real prerequisite: a connected QuantCalc PRO licence or Tax Season Pass, without which it returns no figures and names the free calculator. There is no explicit 'use this instead of X when Y' routing to the five sibling tools.

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, so the description is not required to restate safety. It adds value by disclosing the tool returns both included and excluded items, specifically tax provisions, and provides links. This goes beyond the annotations and gives clear behavioral context about the output content.

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 sentence that packs essential information: what is returned, what is excluded, and that links are provided. There is no filler, and the core 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 no-parameter informational tool with no output schema, the description fully covers what the agent needs to know to call it correctly: the nature of the returned information and the presence of links. Nothing critical 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?

The tool has zero parameters, and the schema is empty with 100% coverage. Per the baseline for no parameters, a score of 4 is appropriate; the description does not need to explain parameters.

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 returns a summary of what the QuantCalc engine models and excludes, including tax provisions out of scope, and links to methodology. This is a specific verb ('Returns') and resource (engine scope), and it differentiates from sibling tools that focus on assumptions, sources, or projections.

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 tool is used to understand modeling scope, but it does not explicitly state when to use it versus alternatives, nor does it provide any exclusion criteria. An agent can infer the purpose, but there is no direct guidance on selecting this tool over siblings.

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 openWorldHint=true, covering safety and scope. The description adds specific behavioral context by specifying what the return includes (which components each publisher provides), going slightly beyond the annotations. It does not contradict annotations and provides useful detail about the operation's 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 a single, efficient sentence that front-loads the main action ('Returns the published capital market assumption sets') and then adds the key detail about components. Every word contributes to clarity, with no wasted phrasing 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?

For a zero-parameter, read-only listing tool with annotations covering safety and open-world behavior, the description provides all necessary context. It tells the agent what the tool returns and what information is included, which is sufficient for correct invocation. No output schema exists, so the description adequately explains the return value.

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 has zero parameters, so the schema trivially covers 100% of parameters. Per rubric, a baseline of 4 is given for 0 parameters. The description does not need to add parameter meaning since there are none, and it correctly focuses on the output semantics.

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 a specific verb ('Returns') and a specific resource ('the published capital market assumption sets the engine carries'), and adds detail about the returned components (returns, volatilities, correlations). This distinguishes it from siblings like compare_return_assumptions (which compares) and run_retirement_projection (which runs projections).

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 provides clear context about when to use the tool: to retrieve the list of available assumption sets and their components. However, it does not explicitly mention alternatives or exclusions relative to sibling tools. The purpose is obvious enough that an agent can infer when to call it, but there is no explicit 'use this when' or 'not for comparing' guidance.

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 cover the safety profile (readOnlyHint=true, openWorldHint=true), and the description adds real behavioral context beyond them: the PRO-licence entitlement requirement and the long-search job_id round-trip. It also discloses the scoring method (after-tax estate minus unfunded spending) and result shape. It stops short of noting cost/runtime expectations of the full search.

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?

Roughly five sentences, front-loaded with the core action and scope, then mechanics, result contents, licence and async handling in a sensible order. The second sentence is dense and runs long, but each clause carries information an agent needs rather than 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 39-parameter simulation tool with no output schema, the description is close to complete: it enumerates the returned statistics (success rate, median paired difference, best age, 10th-90th percentile range), the licence gate and the job_id recall. It omits any note on which inputs are effectively required to get a meaningful search, which the schema (0 required params) cannot supply.

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 all 39 parameters, including nested annuity, ACA and itemized-deduction objects, are already documented. The description adds no parameter-level meaning (e.g. how social_security feeds the restatement) and instead spends its words on mechanics, so the baseline of 3 is appropriate.

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 a specific verb and resource with explicit scope: 'Searches every Social Security claiming age from 62 (or today's age) to 70 for the household's tax-aware plan.' It also states the scoring rule and what the result contains, so an agent can distinguish this age-search tool from the plain projections in the sibling list without opening any 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?

Usage is implied by the purpose ('for the household's tax-aware plan', 'Requires a connected QuantCalc PRO licence'), and the async job_id recall path is explained. However, it never states when to reach for this instead of run_tax_aware_projection or run_retirement_projection, nor any precondition on inputs beyond the licence.

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?

Goes well beyond the readOnlyHint/openWorldHint annotations: it discloses the PRO-licence requirement for bootstrap and historical return models, the silent fallback to parametric without one, and the fact that the result self-reports the return model, path count, and income assumptions including the zero case. This is exactly the kind of behavioural context annotations cannot carry.

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 with the verb and outputs, then the routing and output-content detail. No filler. Slightly dense phrasing ('including when there are none') costs a point 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?

With no output schema, the description compensates by enumerating the return payload (success rate, distribution, assumptions, return model, path count) and the licence-dependent behaviour of two of the three return models. For a 16-parameter simulation tool, an agent has what it needs to call it and interpret the result.

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% across all 16 parameters, including enums and defaults, so the schema does the heavy lifting. The description reinforces the tax caveat on annual_spending but adds no syntax, format, or interaction detail beyond what the schema already documents. 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 resource ('Runs a Monte Carlo retirement projection') plus the engine it runs on and the three outputs it produces (success rate, ending-portfolio distribution, assumptions). It also names the sibling it is not, run_tax_aware_projection, so an agent can separate the two without opening either 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?

Explicitly states the exclusion condition ('Income tax is not modelled by this tool') and routes to the alternative that does model it. It does not address the other plausible siblings (optimize_social_security_claiming, list_return_assumption_sources, compare_return_assumptions), so the routing is clear but partial.

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 cover only readOnly/openWorld, so the description carries real weight and delivers: taxes, Social Security taxability, RMDs, IRMAA and early-withdrawal penalties are paid from the portfolio year by year, and spending is defined as what the household keeps after tax. It further discloses that the result reports every assumption including defaults and what the engine does not model, which is unusually candid scope disclosure. It stops short of runtime/cost hints for a stochastic search.

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, front-loaded with the action and followed by the returned artifacts and tax mechanics; nothing is padded. Given the size of the tool, this is efficiently packaged, though the second sentence is long enough that a clause could be trimmed.

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, 38 parameters and nested objects, the description does describe the returned content and the modelled/not-modelled boundary, which is the right coverage for a computation tool. However, it omits the one thing an agent most needs at selection time: how this differs from run_retirement_projection and when it is worth the heavier modelling.

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% across 38 parameters, several with enums and nested objects, so the schema already documents all input semantics including defaults. The description adds only the framing that accounts are split by tax type; it does not clarify interaction between, say, roth_conversion, withdrawal_strategy and pension_type. 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 gives a specific verb and resource (runs the retirement projection with accounts split by tax type) and enumerates the return payload: withdrawal order, Roth-conversion programme, present-value advantage and range vs drawing traditional first, and after-tax success rate. That is far clearer than a generic 'projection' tool. It never names or contrasts the near-identical sibling run_retirement_projection, so an agent cannot tell the two apart from the description alone.

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?

No when-to-use or when-not-to-use guidance is present; the description only says what the engine computes. The critical routing decision against run_retirement_projection is left entirely to inference, even though the schema's own pension_type field refers to 'the tax-aware tool' as a distinct thing.

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