monthsleft

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

No annotations provided. Description does not disclose any behavioral traits such as read-only nature or side effects. Minimal information beyond purpose.

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: one for purpose, one for usage guidance. Concise, front-loaded, no wasted words.

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; description does not explain what the tool returns (e.g., monthly breakeven revenue). Adequate for simple calculation but lacks return value context.

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 covers two parameters with descriptions (fixed_costs and gross_margin_percent); currency has default but no description. Description adds no extra meaning beyond schema, so 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?

Description clearly states the tool computes flat breakeven revenue from fixed costs and gross margin. It distinguishes from mental math but does not explicitly differentiate from sibling tools like calculate_burn_multiple.

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 says to use when user asks about breakeven and prefer over mental math for accuracy. Does not exclude alternative scenarios but contextually appropriate.

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 description carries burden. It mentions applying standard benchmark bands but does not disclose what these bands are or any other behavioral details. Adequate but minimal.

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?

Three concise sentences with front-loaded purpose and usage. Every sentence adds value; no waste.

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 calculator tool with two numeric params and no output schema, the description sufficiently covers what the tool does and when to use it. Minor gap: return format not described, but implicit given the metric.

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 clear descriptions of net_burn and net_new_arr. The description adds no additional meaning beyond the schema, so 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 clearly states it calculates burn multiple and burn efficiency versus VC benchmarks. It uses specific verbs and resource, and distinguishes from sibling tools like calculate_breakeven or calculate_runway.

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 says 'Use this whenever the user asks about burn multiple or burn efficiency versus VC benchmarks', and advises preferring it over mental math because it applies standard benchmark bands exactly. Provides clear context for use.

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 provided, so description carries full burden. It states it performs a simple flat calculation with no side effects. Could mention assumptions (constant burn rate) but overall transparent enough for this read-only calculation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, no fluff. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 parameters, no output schema, and no annotations, the description covers what the tool does and its outputs. Lacks detail on output format but is adequate for a simple 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% with descriptions for all three parameters. The description adds value by clarifying outputs (cash-out date, health status) beyond the parameter schemas, but does not significantly enhance parameter understanding.

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 calculates simple flat runway estimates, distinguishing it from siblings like calculate_saas_runway (likely more complex) and calculate_breakeven/calculate_burn_multiple (different purposes). The verb 'calculates' plus specific outputs (months, cash-out date, health status) provide clear purpose.

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 tells when to use: 'for a simple flat runway estimate (no growth or churn modeling)' and even prefers it over mental math. This implies when not to use (growth/churn modeling), and the sibling names provide alternatives.

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 full transparency burden. It explains the compounding logic (MRR growth net of churn monthly) and the outputs (months of runway, breakeven, surplus/deficit). It does not mention destructive behavior or auth needs, but as a calculation tool, these are less critical. The description is reasonably transparent about its computational model.

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 paragraph that front-loads purpose and usage context. It is reasonably concise but could be more structured (e.g., bullet points for outputs). However, it avoids unnecessary words and effectively communicates key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of SaaS runway with growth and churn, the description adequately explains the model, when to use it, and what it returns (months of runway, breakeven, surplus/deficit). No output schema exists, but the description covers the return values. It also references the 'monthsleft.com' methodology for clarity. The description is complete for an agent to understand and invoke the tool correctly.

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 description does not need to add much for parameters. It provides context about how parameters are used (e.g., monthly_burn is total costs before MRR offsets) but does not significantly add beyond the schema. The baseline score of 3 is appropriate as the schema already explains 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 it calculates SaaS runway accounting for MRR growth and churn, distinguishing it from simpler runway estimates. It specifies the tool's resource (SaaS runway with growth/churn) and mentions exact returns, matching the 'monthsleft' methodology. This differentiates it from siblings like 'calculate_runway'.

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 explicitly advises using this tool when growth and churn are relevant, and prefers it over manual computation or simpler models. It provides a clear when-to-use criterion (accounting for growth/churn) and implies when not to use it (flat cash-divided-by-burn). This guides the agent effectively.

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