Model Ruler — AI Cost Calculators

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 two modeling behaviors beyond the schema: failure overhead and context growth across turns. However, it never states that this is a pure read-only computation with no side effects, nor what the returned figure represents (currency, breakdown, per-task vs per-month), which matters for a 9-parameter estimator.

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, no waste, and the usage trigger is front-loaded before the modeling caveat. Nothing could be cut without losing 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 a 9-parameter tool with no annotations and no output schema, the description is thin: it does not explain what the tool returns, whether model/provider pricing is looked up or assumed, or how the failure and context-growth adjustments are applied. The schema covers inputs fully, but the return-value burden falls on a description that doesn't carry 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 the schema already documents all nine parameters and their defaults; baseline is 3. The description gestures at two of them ('failure overhead' ≈ success_rate, 'context growth' ≈ context_growth_factor) but adds no syntax, units, or guidance 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 computation ('cost per successful task') and a specific scenario ('multi-step LLM agents'), which separates it from siblings like provider-cost-calculator or token-counter. It never names or contrasts a sibling explicitly, so an agent must infer the boundary, but the verb+resource are 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?

'Use when a user is running multi-step LLM agents and needs cost per successful task' is an explicit trigger condition, not an implied one. There is no when-not guidance and no named alternative among the eleven sibling calculators, 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?

No annotations are provided, so the description carries the full disclosure burden. It usefully enumerates the cost components included (app-action fan-out, MCP tool calls) and excludes LLM token spend, but for a tool with zero annotation coverage it says nothing about the fact that this is a side-effect-free computation and nothing about the shape of the returned estimate.

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 front-loaded sentence with the usage trigger first and zero filler. It is dense but every clause earns its place, with no redundant restatement of the name.

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?

Covers the purpose, the cost components, and the trigger, which is adequate for a 4-parameter calculator. But with no output schema and no annotations, the definition never indicates what the result looks like (a total, a per-component breakdown, currency/units) or how make_modules_per_run derives its default, leaving a real gap for an agent consuming the response.

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% and every parameter already documents its default, so the schema does the heavy lifting. The description adds conceptual framing by linking app-action fan-out and MCP tool-call accounting to the cost model, but supplies no syntax, units, or non-obvious semantics beyond what the schema states.

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 (estimate) and resource (automation-platform cost for an agent workflow) and scopes it by naming the covered components (app-action fan-out, MCP tool-call accounting). It also distinguishes itself from LLM token spend, though it stops short of differentiating from the near-identical siblings agent-loop-cost-calculator and automation-cost-calculator.

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 leading 'Use when a user needs to estimate automation-platform cost for an agent workflow' is a genuine trigger condition, and 'separate from LLM token spend' implicitly routes token-cost questions elsewhere. However, no alternative sibling is named and there is no guidance on when this calculator beats agent-loop-cost-calculator or automation-cost-calculator.

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 behavioral burden. It says nothing about what is returned (total vs per-platform breakdown), pricing/currency assumptions, region, or whether the result is a pure computation. For a calculator with zero annotation coverage this is a significant gap.

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 well-formed sentence, front-loaded with the usage trigger. It is tightly written and wastes no words, though it is almost entirely trigger phrase with no behavioral payload.

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 annotations and no output schema, the description needed to convey return shape and pricing assumptions, which it omits entirely. Given the dense cluster of sibling cost calculators, the definition is too thin for the agent to call or interpret it confidently.

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 three parameters are already documented in the schema, and the baseline is 3. The phrase 'recurring workflow shape' loosely hints at runs-per-month and steps-per-run, but adds no syntax or defaulting detail 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 clear verb (compare cost) and resource (workflow automation platform cost), and names the three platforms in scope (Zapier, Make, n8n). It does not explicitly distinguish itself from the many sibling cost calculators, though the Zapier/Make/n8n domain is reasonably distinctive.

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 'Use when...' clause supplies a trigger condition, which is better than nothing. However, no alternatives or exclusions are given, and with siblings like agent-workflow-cost-calculator and provider-cost-calculator the agent gets no help deciding between overlapping cost 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?

No annotations are supplied, so the description carries the full behavioral burden. It conveys that this is an analytical/advice operation and enumerates the strategies it can recommend, but says nothing about side effects, defaults applied (overhead 2000 / output 1000), or what the verdict looks like. For a stateless calculator the risk is low, but the disclosure is thin.

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 front-loaded sentence that leads with the trigger condition and packs both the fit-check and strategy-recommendation purposes with zero 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?

There is no output schema, and the description never states what the tool returns (a boolean verdict, a token headroom number, a ranked strategy list). With five parameters and no annotations or output contract, the definition leaves the agent guessing about the response shape even though the schemas cover inputs well.

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 already documents all five parameters including defaults and the strategy enum. The description only loosely mirrors the inputs ('document plus prompt plus output') without adding format or precedence rules beyond the schema, 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?

The description states a specific capability: determining whether a document plus prompt plus output fits a model's context window, and returning a strategy recommendation from a named set (truncate/summarize/rag/chunk). That is concrete and distinguishable from the cost-calculator siblings, though no sibling is named explicitly for contrast.

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 opens with an explicit 'Use when...' trigger covering two conditions: a fit check and a strategy recommendation. There is no statement of when NOT to use it or which sibling to pick instead, so the routing guidance is clear but not complete.

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 provided, the description carries the full behavioral burden. It usefully discloses the return contents (total cost, per-run cost, parallel time estimate) and the sample/model/trial inputs, but says nothing about side effects, determinism, or whether it is a pure local calculation versus a billable API call.

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 tight sentences: the usage trigger is front-loaded, followed by a compact statement of inputs and outputs. No filler or restated name.

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 enumerates the three return values, and all 10 parameters are documented in the schema. For a stateless calculator this is nearly complete; only cost-model assumptions (provider pricing, judge pass effects) are left implicit.

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% and every parameter has a documented default, so the schema already does the heavy lifting. The description only echoes 'samples/models/trials' and adds no syntax, units, or interaction details beyond that 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?

The description gives a specific verb+resource ('budget an LLM evaluation run') and the domain term 'eval' inherently separates it from sibling calculators like fine-tune-roi-calculator or rag-pipeline-cost-calculator. It stops short of explicitly naming which sibling to use instead, so it does not reach 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?

'Use when a user needs to budget an LLM evaluation run' is a clear trigger condition that tells the agent the right context. There is no guidance on when NOT to use it or which of the many sibling cost calculators to prefer, so it misses the exclusions a 5 would require.

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 and no output schema, so the description carries the disclosure burden — and it does disclose the four computed return values, which is the main behavioral fact for a deterministic calculator with no side effects. It omits how optional cost inputs default when omitted, a minor gap 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?

Two sentences, zero filler, with the triggering condition front-loaded and the return contract immediately after. 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?

With no output schema, the description correctly supplies the return values, and the six parameters are fully covered by the schema. It is nearly complete for a stateless calculator; only the defaulting behavior of the optional cost inputs is left unstated.

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 is already documented in the schema, including the default note for prompt_reduction_pct. The description adds no parameter-level meaning beyond that, so the 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 states a specific decision-support purpose (fine-tuning vs prompt engineering) and enumerates the exact outputs (training cost, monthly inference savings, months-to-ROI, breakeven volume). It is clearly distinguishable from generic cost calculators by its ROI/breakeven framing, though it never names a sibling tool 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?

'Use when a user is considering fine-tuning vs prompt engineering' gives a clear triggering condition. It does not name alternatives such as self-host-breakeven-calculator, which an agent might reasonably confuse it with, 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.

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

No annotations are provided, so the description carries the burden. It discloses the key behavior that retention affects the result and that output is a monthly cost, which is meaningful for a calculator. However it omits any mention of defaults (avg_log_bytes 4096, retention 30 days) or output structure, leaving gaps.

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 tight sentences with the usage condition front-loaded and the return behavior second. Nothing is wasted, though it is minimal rather than richly structured.

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 pure calculation tool with no annotations and no output schema, the description is adequate but thin. It tells the agent what is returned (monthly cost) but not the output shape (single figure vs breakdown) or that defaults apply, which would help callers interpret results.

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 already documents all four parameters and their defaults. The description adds only the general notions of 'request volume' and 'retention adjustment', which map to but do not enrich the schema. Baseline 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?

States a specific resource (LLM observability tooling cost) and an implied verb (compute monthly cost), which cleanly separates it from siblings like agent-loop-cost-calculator or eval-cost-calculator. It does not explicitly contrast itself with provider-cost-calculator, which could be confused since 'provider' here means observability vendor, keeping it 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?

"Use when a user needs to budget LLM observability tooling" gives a clear trigger condition, but names no alternatives or exclusions among the many sibling cost calculators. An agent must infer the routing itself.

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, so the description carries the full behavioral burden. It usefully discloses the return shape (monthly cost plus a tier comparison table) and the input basis, but says nothing about whether this is a pure read-only computation, whether results are cached, or pricing-data freshness.

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 usage trigger followed by the input/output summary. Every clause earns its place with no 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?

No output schema, but the description compensates by stating what is returned (monthly cost plus tier comparison table). All six parameters are schema-documented and the usage context is covered; only pricing-source/freshness caveats are absent, which is minor.

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 already documents all six parameters including the include_comparison default. The description paraphrases tokens-per-call, call volume, provider/model, and the comparison table but adds no syntax, format, or default 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+resource: computing LLM workload cost for a specific provider/model, with an explicit comparison mode. It partially differentiates from the crowded sibling set (all cost calculators) by naming 'provider/model' scope, but does not contrast itself against agent-loop-cost-calculator, eval-cost-calculator, etc.

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 clear triggering condition ('when a user asks what an LLM workload costs on a specific provider/model, or wants to compare cost across providers'). No when-not guidance and no named alternatives, but the trigger is unambiguous.

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 behavioral burden. It helpfully discloses the return contents (VRAM, speedup, quality delta) despite no output schema, but says nothing about side effects, determinism, or whether it is a pure read-only computation. Adequate but leaves gaps for a no-annotation 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?

Two tight sentences with zero waste, and the invocation condition is front-loaded before the output description. 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 stateless calculator with only one required parameter and full schema coverage, the description is nearly complete: it explains when to call it and what it returns in lieu of an output schema. It could still mention that unspecified precisions default to bf16/int4, but the schema covers that.

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%, with all five parameters documented including defaults and enum values, so the schema does the heavy lifting. The description only references 'parameter count and precision transition,' adding no format or unit detail beyond what the schema already states.

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 action (calculate quantization impact) with named outputs: VRAM requirement, speedup estimate, and quality delta. The resource and scope are clear, but the description never explicitly contrasts itself with the many cost-calculator siblings, so differentiation relies on the tool name 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?

Provides a concrete trigger: 'Use when a user is planning to quantize an LLM to fit on smaller hardware.' This is clear context for invocation, but it names no alternatives or exclusions among the 11 sibling calculators, so an agent has no routing guidance if the request straddles quantization and cost analysis.

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 behavioral burden. It does disclose the return shape (monthly cost, breakdown, dominant-component identification), which is genuinely useful, but says nothing about defaults being applied on omitted optional params, determinism of the estimate, or treating defaults such as answer_tokens/chunks_retrieved.

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 invocation trigger is front-loaded ahead of the return description. Nothing could be cut without losing signal.

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 job of describing the return value (monthly cost with breakdown and dominant component), which is what an agent needs to decide to call it. Remaining gaps are behavioral detail on defaults and estimation assumptions rather than anything blocking 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 100% across all 11 parameters, including enum values for vector_store and embedding_provider and defaults for the optional sizing params, so the schema does the heavy lifting. The description adds no parameter-level detail beyond that, making the baseline 3 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?

States a specific verb+resource ('end-to-end RAG cost estimation') and enumerates the covered cost components (embedding + vector store + generation), so the scope is unambiguous. It does not explicitly contrast itself with the many sibling cost calculators, but the RAG-pipeline framing is distinctive enough to route correctly.

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?

Opens with an explicit trigger: 'Use when a user needs end-to-end RAG cost estimation.' That gives clear context for invocation, but offers no when-not condition and does not name alternatives (e.g., provider-cost-calculator for single-provider comparisons, self-host-breakeven-calculator for hosting decisions) despite eleven overlapping 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?

No annotations exist, so the description carries the full burden. It usefully discloses the returned artifacts (breakeven volume, cost comparison, recommendation), which is the key behavior for a tool with no output schema, but it never states that the tool is a stateless computation, nor mentions defaults or assumptions for the optional inputs.

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 waste, with the use-condition front-loaded and the output summary second. Appropriately sized for a single-purpose calculator.

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 annotations, the description must convey returns, which it does. Six parameters are all documented in the schema. Only the omission of computation assumptions (defaults, statelessness) keeps it from a 5.

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 already documents all six parameters and their defaults (utilization 60%, overhead 40%). The description adds no parameter meaning beyond that, making the baseline 3 correct.

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 pairs a clear trigger ('deciding between API usage and self-hosted GPU inference at a given volume') with a specific output set (breakeven token volume, cost comparison, go/no-go recommendation). This distinguishes it well from the cost-calculator siblings, though it never names an alternative tool 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 an explicit when-to-use condition scoped to a decision context, which is stronger than most calculators. It offers no when-not guidance or named alternative, 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?

No annotations, so the description carries the burden. It usefully discloses the output shape (range plus byte measurements), but says nothing about determinism, approximate-vs-exact counting, or which tokenizer is assumed by 'default' — gaps for a tool with zero annotation coverage.

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 waste, front-loaded with the triggering condition before the behavior. 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 three-parameter, no-output-schema tool, the description covers both when to call it and what it returns, which is sufficient to invoke correctly. The only missing piece is tokenizer-selection guidance among the seven enum values.

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 already documents all three parameters including the tokenizer enum. The description only echoes 'text and tokenizer family' and ignores expected_out_tokens, adding no syntax or semantics 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+resource (counts tokens for given text) and quantifies the return ('low/high token range and byte-level measurements'). It is clearly a measurement primitive, distinguishable from the sibling cost/planning calculators.

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 explicit trigger conditions ('when a user asks how many tokens... or needs to estimate prompt size before pricing a workload'). It does not name alternative tools or when NOT to use it, but the intended context is unambiguous.

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