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

With no annotations provided, the description carries the full burden. It discloses exactly what the tool checks and its binary output. Since this is an audit function, it implicitly signals a read-only operation with no side effects. It does not explicitly mention auth, rate limits, or lack of mutation, but for a tool of this type, the description is transparent enough. A 5 would require explicit side-effect statements, but the nature of 'audit' makes them unlikely.

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 lists the audit criteria and output. Every word adds value, with no redundant phrases. It is well-structured and front-loaded with the main action.

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 one parameter, no output schema, and simple boolean output, the description provides all necessary information: input (subject line), what it checks, and the result format. The sibling tools are unrelated, so no further context is needed. The description is complete for its complexity level.

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 schema only defines a single 'subject' string parameter, and schema description coverage is 0%. The description clarifies that 'subject' refers to an email subject line, which adds necessary context. However, it doesn't add details about length limits, encoding, or other constraints. Since there is only one parameter and the schema is minimal, the description partially compensates but not fully, scoring a 3.

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 specific action ('Audit an email subject line') and the specific criteria being evaluated (mobile truncation length, all-caps, spam phrases, fake Re:/Fwd:, specificity). It also states the output format ('Returns PASS/NEEDS WORK'), fully distinguishing it from sibling tools which focus on packs, growth loops, and other unrelated functions.

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 clear use: to audit an email subject line for the listed issues. While it doesn't explicitly mention alternatives or exclusions, the sibling tools are clearly different domains (packs, growth loops, metrics), so the appropriate context is evident. The omission of explicit 'use when' statements prevents a 5, but the context is clear.

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 full behavioral disclosure responsibility. It transparently lists what is included in the payload (growth loops, welcome sequence, metric reads), which is valuable, but it does not disclose any potential size, side effects, or operational characteristics such as read-only status or rate limits.

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, front-loaded sentence that immediately states the tool's purpose and contents. Every word earns its place, with no redundancy or 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 zero-parameter tool with no output schema, the description sufficiently explains what the tool returns (the three named components). It lacks an explicit statement about the payload format or any caveats, but given the simplicity and the inherent clarity of the tool, it is reasonably complete. A small deduction for not explicitly stating that this returns all data at once or any potential size implications.

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 already reflects this (100% coverage). Per the rubric, a no-parameter tool receives a baseline of 4. The description adds no parameter-specific details, which is appropriate since there are none to clarify.

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 identifies the tool as returning a combined newsletter library payload, explicitly listing its components (growth loops, welcome sequence, metric reads). This distinguishes it from sibling tools that individually cover these areas, though it lacks an explicit action verb, relying on the tool name for the 'get' semantics.

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 phrase 'complete newsletter library in one payload' implies using this tool when you want the entire collection at once, as opposed to individual sibling tools. However, it does not explicitly state when to choose this over alternatives, nor does it mention any exclusions or supplementary 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 output content (mechanism, when it wins, execution rules, failure mode) and the behavior when omitting the parameter. However, 'selection logic' is ambiguous and there is no mention of return type, pagination, or other behavioral details that could affect 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 two sentences, concise and front-loaded. The first sentence states the tool's purpose and output content; the second lists valid values and explains omission behavior. Every word 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 only one simple parameter and no output schema, the description is fairly complete. It covers the main purpose, output content, and parameter semantics. The 'selection logic' phrase is under-specified, but overall it provides enough context for an agent to use 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?

The schema has one parameter with an enum but no description (0% schema coverage). The description compensates by listing the valid loop values and explaining that omission returns all loops with selection logic. It adds meaning beyond the schema, though it doesn't detail each loop type.

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 gets a newsletter growth loop and lists the specific content (mechanism, when it wins, execution rules, failure mode). It distinguishes from siblings by focusing solely on growth loops, and the enumerated loop types add specificity.

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 guidance on parameter use: include a specific loop or omit for all loops plus selection logic. While it doesn't explicitly contrast with sibling tools, the scope is self-evident. It lacks explicit when-to-use versus alternatives, but the context is sufficiently clear.

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 behavioral disclosure burden. It reveals the output type (readiness verdicts for three paths) and the underlying heuristic ('Engagement monetizes; size impresses'), but it does not describe the exact verdict format, possible values, or edge-case behavior (e.g., what happens if only open_rate is provided without click_rate). This is meaningful but incomplete context.

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 exceptionally concise: two sentences that pack a full specification. It front-loads the input and output, lists the three monetization paths, and ends with a compact heuristic. Every sentence earns its place with zero fluff.

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 moderate-complexity tool with no output schema and no annotations, the description covers inputs, outputs, and decision logic well. It names all three monetization paths and mentions sequencing. However, it lacks explicit detail on the structure of the verdicts (e.g., categorical vs. numerical) and does not clarify whether open and click rates are independent optional flags, leaving minor ambiguity for an agent.

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 input schema already provides 100% parameter descriptions, so the description adds little beyond what structured data provides. The phrase 'as percents' mirrors the schema's '%' descriptions without enhancing meaning. Per calibration, baseline is 3 since schema coverage is high and no critical parameter detail is missing.

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: given subscriber count and optional open/click rates, it returns readiness verdicts for three specific monetization paths. It uses a specific verb ('returns readiness verdicts') and distinguishes itself from sibling tools by focusing on monetization readiness, not general growth or subject line issues.

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 when to use the tool by specifying required inputs ('Given subscriber count...'), but it does not explicitly state when to choose this tool over alternatives or provide exclusion criteria. No comparison to sibling tools is offered, so guidance is basic but not fully developed.

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 full burden for behavioral disclosure. It does not mention whether the tool is read-only, whether authentication is required, what the response format is, or any side effects. The description focuses purely on the informational content of the welcome sequence, not on how the tool behaves when invoked.

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 with a clear structure. The arrow-separated stages (deliver → receipt → quick win → segment → soft offer → close) convey the sequence progression efficiently. Every phrase earns its place, and the sentence is front-loaded with the core subject.

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 zero parameters and no output schema, the description does a strong job of defining the content scope: per-email job, timing, subject shape, and the one-job-per-email rules. However, it doesn't explicitly state that the tool returns this information or describe the response format. The essence is well-captured, but a brief mention of return behavior would make it fully complete.

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 input schema has zero properties, so there are no parameters for the description to elaborate on. The description correctly focuses on the output scope. A baseline score of 4 is appropriate for zero-parameter tools, and the description adds value by outlining what the response will cover.

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 identifies the subject as the 6-email welcome sequence architecture and specifies its components (per-email job, timing, subject shape, rules). The arrow progression (deliver → receipt → quick win → segment → soft offer → close) makes it distinct from siblings. It lacks an explicit action verb like 'retrieves' or 'displays', but the name 'get' implies the action.

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 no guidance on when to use this tool versus alternatives such as get_full_pack or get_growth_loop. It only describes the content itself, with no context, prerequisites, or exclusions. The agent is left to infer usage solely from the name and the fact that it covers welcome sequence architecture.

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 burden. It discloses the return values (healthy range, warning threshold, diagnostic) but does not mention whether the tool is read-only, potential errors, or other behavioral nuances.

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, front-loaded with the verb 'interpret', and includes only relevant information. Every word 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 tool with a single enum parameter and no output schema, the description adequately covers purpose, input, and return values. The agent can determine when to use it and what to expect.

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%, but the description lists the exact parameter values and explains their context ('practitioner ranges'), adding semantic meaning beyond the raw enum. It compensates well for the low schema coverage.

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 action ('Interpret a list metric'), specifies the target category ('practitioner ranges'), and lists all supported metric types. It is specific and distinct from sibling tools which focus on different operations.

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 usage when you have one of the listed metrics, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. No alternative tools are mentioned.

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