Agency Growth

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the agent knows this is a safe, local informational read with no side effects. The description adds only a light sense of the returned content and no further behavioral context (e.g., static vs. dynamic content), which is acceptable given annotation coverage but not enriching.

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 compact sentences with the subject and the usage trigger front-loaded. No filler or redundancy; every clause carries 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?

For a no-parameter, no-output-schema informational tool, the description covers what the tool is and when to call it. It could be slightly more complete by noting the result is descriptive text rather than structured data, but nothing essential 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 takes zero parameters, so the baseline is 4. There is nothing for the description to clarify beyond confirming the no-argument nature of the call.

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 (Agency Growth / agencygrowth.com) and what the tool conveys about it — what it does, who it's for, how it helps. It is distinguishable from siblings like list_plans and run_ai_audit, though it does not explicitly name or contrast them.

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 the user asks about Agency Growth" gives an explicit trigger condition, which is the main guidance needed for a simple informational lookup. It does not state when NOT to use it or name alternatives, 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?

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds genuinely non-obvious operating behavior: it must interrogate the user one question at a time rather than inferring or fabricating answers. This is valuable agent-facing guidance beyond the annotations, though it says nothing about latency, failure modes, or output shape.

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, each doing distinct work: what it produces, how to elicit inputs, and when to invoke. Front-loaded with the outcome and 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?

With no output schema, the description compensates by naming the three deliverables (growth stage, priority gap, week-by-week plan), and the inputs are fully specified in the schema. An agent has everything needed to select and sequence the call 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% with enums fully enumerated for all five parameters, so the schema carries the semantic load. The description only refers to 'five answers' generically and adds no parameter-level detail. 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 transformation with concrete outputs: five answers in, growth stage + first gap + week-by-week 30-day plan out. This is clearly distinguishable from siblings like agency_pricing_benchmarks or get_playbook, which produce different artifacts.

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?

Names the trigger conditions explicitly (agency owner asking what to focus on next, or why growth stalled), which is a clear when-to-use. It does not name an alternative tool or a when-not-to-use case, 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real behavioral context: inputs are optional and omitting service yields every range, and the result is a diagnostic (whether underpriced + annual gap). It doesn't describe the underlying data source's recency or the response shape, so not 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?

Three tight sentences, front-loaded with the data offering before the mechanics and the trigger. The parenthetical service list mildly duplicates the schema enum, a small redundancy that keeps it from a 5.

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 read-only benchmark tool with no output schema, the description covers the inputs, the derived outputs, and the trigger condition, which is what an agent needs to call it correctly. Remaining gaps (unit/currency confirmation, how ranges are returned) are 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% with a fully documented service enum, so the schema does the heavy lifting and the baseline is 3. The description restates the input pattern ('service and the user's current retainer and client count') without adding format, units, or handling details beyond what the schema already 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?

Names the concrete resource (published monthly retainer ranges per service) and the specific analytical outcome (underpriced detection plus annual gap value). It is clearly distinguishable from siblings like read_guide or run_ai_audit, which share no such benchmarking 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?

Gives an explicit trigger: 'Use when the user asks what to charge or whether their prices are too low.' That is a clear when-to-use context. It stops short of naming an alternative tool or stating when not to use it, 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful payload context by enumerating the playbook's sections (owner quotes, tools, gap, steps, metrics, guide links, agent), which tells the agent what a successful read contains, but it says nothing about auth, error behavior on an invalid slug, or caching.

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 opens with the scoping fact ('One playbook in full') before the content list. The long enumeration is dense but each item is informative about the return payload, so it largely 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 usefully compensates by describing what the playbook contains section-by-section, so the agent knows what it will receive. Combined with a fully documented enum parameter and read-only annotations, the definition is nearly complete; only usage routing and failure behavior are absent.

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 the single parameter is fully documented with an enum and a source reference, so the description needs to add nothing. Baseline for a one-parameter, fully-covered schema is 4; the description does not degrade it.

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 the resource precisely — a single playbook in full — and enumerates its sections, which implicitly distinguishes it from list_playbooks in the sibling set. However, it never uses an explicit retrieval verb or names the alternative, so the agent must infer that this is the fetch-by-slug counterpart to the listing tool.

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 offers no when-to-use guidance, no prerequisites, and no mention of alternatives. The only routing hint is in the schema ('Playbook slug from list_playbooks'), which is structured-field territory, not description content.

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=false, so the safety profile is covered. The description adds real behavioral value on top: publication cadence ('each weekday'), per-issue structure, and result ordering ('Newest first'). It does not discuss the limit/pagination effect on results, which is the only material 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?

Three compact sentences, front-loaded with what the tool returns, then ordering, then the usage cue. The list of issue components is slightly dense but each element tells the agent what it can expect back, so little 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?

For a simple read-only, single-parameter list tool with no output schema, the description adequately conveys what comes back and when to reach for it, and annotations cover the safety profile. A brief note on how many items are returned by default vs. the limit would make it fully self-contained.

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?

Only one parameter (limit) and the schema documents it fully, including the 1-30 range and default of 5. The description adds no parameter-level detail beyond that, so the baseline 3 for high schema coverage 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 ('Recent AgencyGrowth Daily issues') and then describes the actual payload of each issue (job to hand off, tool used, recipe, the catch), which is far more informative than a restated name. It doesn't explicitly name or contrast sibling content tools like get_playbook or read_guide, but the resource is distinct enough that an agent can separate them.

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 condition: 'Use when the user wants a concrete way to automate or delegate a recurring agency task.' That is a clear use context, but there is no explicit when-not guidance and no mention of the alternative siblings (get_playbook, list_playbooks) an agent could pick instead.

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=false, so the safety profile is covered. The description usefully adds the content scope (plans, prices, starting points) but says nothing about return format or pagination. With annotations carrying the behavioral burden, a 3 is appropriate.

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 short sentences, front-loaded with what the tool returns and followed by the invocation trigger. No filler or repetition.

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, read-only lookup with no output schema, the description states what data comes back and when to call it, which is sufficient. A note that no arguments are needed or how results are grouped would be the only minor addition.

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 takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 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 names a specific resource (Agency Growth's plans and prices) and states what is returned, including 'where each one starts'. The verb 'list' is implied rather than stated, and it does not explicitly distinguish itself from siblings about_agency_growth or run_ai_audit, but the resource is distinct enough to be identifiable.

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 trigger: 'Use when the user asks about pricing or plans.' That is a clear context for invocation. It offers no exclusions or named alternatives, 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered by structured fields. The description usefully adds content framing (free, 30-day, one-per-bottleneck) and the intended two-step workflow, but says nothing about the shape or ordering of the returned list. Given annotation coverage, a 3 is appropriate.

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 resource definition and closed with the usage condition and routing. Every clause carries information; nothing is redundant with the title or annotations.

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 list tool with no output schema, the description supplies everything an agent needs: what the list contains, the categories it covers, when to call it, and which tool to call next. Nothing material 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 takes zero parameters, so there is no parameter surface to document and the baseline is 4. The description does add meaning about what the call yields (the five bottleneck categories), which is the closest thing to 'inputs' this tool has.

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 the concrete resource (Agency Growth's free 30-day playbooks) and its organizing principle (one per growth bottleneck), then enumerates the five bottlenecks. It also names the sibling get_playbook as the follow-up, so an agent can distinguish the list step from the retrieval step 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?

Explicit trigger condition ("Use when an agency owner asks what to fix first or how to fix a stall in one of those areas") plus an explicit next step ("then call get_playbook"). Both when-to-use and the alternative/routing are stated rather than inferred.

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=false, so the safety profile is covered. The description adds useful return context (sources and last-updated date), but says nothing about permissions, rate limits, or content length.

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 short, front-loaded sentences with zero waste: what it returns, how to invoke it, and how to cite it. Every sentence 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 one-parameter read tool with full schema coverage and annotations, the description is complete: it explains the return contents (since no output schema exists) and the citation requirement. Nothing an agent needs 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% and the single slug parameter is already documented with an example. The description's instruction to 'Pass a slug from search_guides' reinforces but does not extend 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 ('Full text of one Agency Growth guide') and names the return components (sources, last-updated date). It also distinguishes itself from the sibling search_guides by telling the agent to pass a slug obtained there.

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 clear context: use this after finding a slug via search_guides. It doesn't state explicit exclusions or alternatives beyond that, so it falls short of a full when/when-not statement.

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?

Adds real behavior beyond the annotations: ~1 minute runtime (latency), the email consent requirement implying an email side effect, and the shape of the return. The annotations already signal openWorldHint=true and non-destructive, so the added runtime and consent context is meaningful. It could be stronger by stating that the data is submitted externally.

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?

Front-loaded with the action in the first sentence, then prerequisites, timing, and return value in short clauses. Every sentence earns its place, though the middle instruction is dense enough that it reads more as a checklist than prose.

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-param, 7-required, no-output-schema tool, the description supplies the missing return shape, timing, and consent preconditions. Nothing an agent needs in order to invoke it correctly is absent.

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 field including the enum bands is already documented. The description only adds a global rule ("never guess or invent any of them") rather than per-parameter meaning. Baseline 3 is appropriate when the schema carries the 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?

States a specific verb+resource ("Run Agency Growth's free AI audit of the user's business"), naming the actor and the object. It is trivially distinguishable from the informational siblings about_agency_growth and list_plans, since this is an action tool. No ambiguity about what gets produced.

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?

Explicit invocation gate: "only call this once the user has given their name and email and agreed to receive the result by email," plus "Ask the user for each field first; never guess or invent any of them." This tells the agent both when it may call and what it must collect beforehand — no alternatives need naming because the siblings do unrelated things.

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=false, so safety is covered. The description adds genuine behavioral context beyond them: the result shape (guides with a short summary and slug) and the empty-query behavior. It stops short of describing result counts or ranking, but adds real value.

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?

Front-loads the resource and scope, then usage, then the follow-up tool. The topic enumeration is slightly list-heavy but serves discoverability for a search tool, so it earns its space; no sentence is redundant.

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, so the description carries the return-value burden and does so ('matching guides with a short summary and slug'). With annotations covering safety and the schema covering the parameter, an agent has everything needed to call this 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 coverage is 100% and the single query parameter is well documented in the schema, so the baseline is 3. The description goes further by documenting the empty-query semantics ('An empty query lists every guide'), which is not in the schema and directly affects how the parameter is used.

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 (search) and resource (Agency Growth's guides) and enumerates the covered topics, which makes its scope unmistakable. It also names the sibling read_guide as the follow-up for full text, so an agent can place it precisely among peers.

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 an explicit trigger ('Use when the user asks how to price, grow, or run a marketing or AI agency') and an explicit next step ('then call read_guide for the full text'). The edge case for an empty query is also spelled out, leaving nothing to inference.

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