growthmak

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

The description adds significant behavioral context beyond the readOnlyHint annotation: it specifies the tool cannot perform bookings or submit details, and warns against claiming a booking. This is a model of transparency for a read-only information retrieval 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, no wasted words. The first sentence front-loads the core purpose, the second provides critical usage constraints. 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 tool with no parameters and no output schema, the description fully covers what the agent needs: what it returns, what it does not do, and how to use it. Sibling tools are distinct, and the context is 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?

With zero parameters and 100% schema coverage, the description does not need to elaborate on parameters. It mentions the return value (bookingUrl, session coverage, what they leave with), which adds value for the agent. Baseline 4 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 the tool returns a booking link for a free growth audit plus session details. It uses specific verbs ('return the link') and distinguishes from sibling tools like find_case_studies or get_packages which serve different purposes.

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 warns the agent that it cannot book a slot or submit contact details, instructing to send the user to the returned URL. This provides clear guidance on when to use the tool (for getting the link) and when not to claim a booking, though it does not name alternative tools for booking (which none exist).

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 burden of behavioral disclosure. It transparently states two outcomes: returns 'served: false' with a full list of served markets when a country is not served, and returns local proof points when it is. This covers key behaviors, though details on proof point format are omitted.

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 that front-load the purpose and immediately follow with behavioral guidance and a warning. Every word earns its place; there is no fluff or repetition. It is as concise as possible while remaining informative.

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 tool's simplicity (one parameter, no output schema, no annotations), the description adequately covers the tool's functionality and return behavior in both success and failure cases. It lacks fine-grained detail about the structure of proof points or the served market list, but the core information is complete enough for an agent to use the tool effectively.

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 describes the only parameter 'country' as 'Country or region name' (100% coverage). The description adds no additional semantic information about this parameter beyond restating its purpose, so it meets the baseline expectation without adding extra value.

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 verb 'Check' and resource 'market coverage' for a given country or region. It distinguishes from sibling tools by focusing on coverage verification rather than auditing, case studies, or other functions. The purpose is specific and 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?

The description explicitly advises 'do not infer coverage from other pages,' providing a clear guideline to use this tool rather than deriving coverage information elsewhere. It gives context on when to use the tool but does not name specific sibling alternatives or state when not to use it.

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 full burden. It discloses loose matching behavior with examples ('manufacturing' matches 'Manufacturing · Construction'), explains the return content, and advises against guessing filters. It could mention sorting or pagination, but the provided details are clear and sufficient.

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 three sentences, each essential. The first sentence states the verb and resource, the second clarifies the use case, and the third explains matching behavior. 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?

Given the tool's simplicity (zero required params, no output schema), the description fully covers what an agent needs: what it does, what it returns, how filtering works, and when to use it. It 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 description adds significant meaning beyond the input schema: it explains the loose matching logic, provides concrete examples for market and industry, and explicitly advises to omit rather than guess parameters. Schema coverage is 75%, and the description compensates fully for the remaining gaps.

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 finds Growthmak client case studies filtered by industry, market, or service, and specifies the return fields (challenge, approach, results). It distinguishes from sibling tools by focusing specifically on case studies rather than audits, coverage checks, or packages.

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 states the use case: 'Use this to answer "have you worked with businesses like mine?"' and advises omitting filters rather than guessing. However, it does not mention when not to use this tool or directly compare to 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 are provided, so the description carries the full burden. It discloses that the tool returns explanatory text about the system layers and services. However, it does not mention if the tool is read-only, whether it requires authentication, or if there are any rate limits. For a knowledge retrieval tool, the absence of these details is acceptable but prevents a higher score.

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

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

The description is a single sentence that packs a lot of information: what the tool does, how to get different outputs, and scope of the output. It is front-loaded with the purpose. While concise, it could be slightly clearer with a small break between the two usage modes, but overall it is efficient.

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 tool has one optional enum parameter, no output schema, and no annotations, the description is sufficient for an agent to understand its use. It explains both invocation modes and the scope of results (services within a layer). It does not need to detail return values since they are explanatory text. The description is complete enough for an agent to 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 0%, but there is only one parameter with enum values. The description adds significant meaning beyond the schema by explaining the effect of including versus omitting 'layer'. The enum values are listed in the schema, so no further explanation of each layer is needed. The description effectively compensates for the lack of schema descriptions.

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 what the tool does: explain the Growthmak Growth Engine System. It distinguishes two distinct use cases: get an overview of all five layers (by omitting 'layer') or get detail on a specific layer (by passing its name). This differentiates it from sibling tools like 'book_growth_audit' or 'find_case_studies'.

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 tells when to omit the parameter (overview of all layers) and when to pass it (for detail on a specific layer). It does not name alternative tools explicitly, but the context of sibling tools is present and the usage distinction is clear enough for an AI agent to decide between calling this tool with or without the layer, and to know not to use it for auditing or case studies.

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 burden. It discloses that the tool returns scope details but not prices, and that fees follow a free diagnostic. This gives the agent useful behavioral context beyond a simple read operation.

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 pack all essential information: what the tool retrieves, what details are included, usage variant for listing all, and a behavioral note on pricing. 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?

Given no output schema or annotations, the description covers the tool's purpose, parameters, and behavioral nuance well. It could mention whether the output is structured in a standard format, but for a simple retrieval tool, this is mostly 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?

Schema description coverage is 0%, so the description must compensate. The description explains that the 'name' parameter is optional and omitting it lists all packages, adding value beyond the schema's enum constraint.

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 retrieves engagement packages, names all three packages, and specifies what details are included (what is included, timelines, who each fits). It effectively distinguishes itself from siblings like book_growth_audit or get_growth_engine.

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 says to omit 'name' to list all three, and notes that no prices are published, guiding usage expectations. It does not explicitly name alternatives for when the user needs pricing, but the context is implied.

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 that the output is Markdown and that the slug must be used. However, it does not mention error behavior (e.g., what happens with an invalid slug), authentication needs, or rate limits. For a simple read operation, this is adequate but leaves some behavioral unknowns.

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 extremely concise: two sentences. The first sentence defines the core purpose and includes examples; the second provides a usage workflow. Every word is purposeful, and the most critical information (verb, resource, identifier format) comes first.

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 retrieval tool without an output schema, the description is nearly complete. It covers the input source, input format, and output format (Markdown). It could be improved by explicitly stating what the return value contains (e.g., full page content), but this is largely implied. The examples and workflow guidance cover the main use case.

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 a description for the 'slug' parameter. The description adds significant value by giving concrete examples of valid slugs and explicitly instructing the agent to derive the slug from search_growthmak, going beyond the schema's minimal description.

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 action ('Retrieve a Growthmak page as Markdown'), the resource ('Growthmak page'), and the identifier ('given its slug'). It provides multiple examples of valid slugs, making the purpose unmistakable. The examples also help distinguish this tool from sibling tools like search_growthmak or get_packages.

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 instructs the agent to use the URL returned by search_growthmak to build the slug, establishing a clear workflow. While it does not explicitly state when not to use this tool (e.g., for non-page resources), the context of sibling tools makes the appropriate usage 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 burden. It discloses the return format ('pages with titles, URLs and excerpts'), but does not mention whether the operation is read-only, side effects, or any other behavioral details beyond the output. For a search tool, this is acceptable but slightly limited.

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: purpose and scope, usage guidelines with alternatives, and output summary. Every sentence serves a distinct purpose, no wasted words, and important information is front-loaded.

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

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

With 3 parameters, no output schema, and no annotations, the description covers purpose, usage, and output format reasonably well. It does not explain pagination or section filtering details, but the schema covers those. The description is sufficient for the tool's complexity.

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 only 33% (query has a description). The description adds value by listing the content types that map to the 'section' parameter, but does not elaborate on 'limit' or the 'query' format beyond what the schema provides. Given low coverage, more compensation would be ideal.

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 searches all Growthmak content and lists specific content types (services, packages, case studies, blog posts, FAQs, free tools). It uses the verb 'search' and explicitly names more specific sibling tools, distinguishing its broad scope.

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 instructs to use this tool only when no more specific tool fits, and lists four alternatives: get_growth_engine, get_packages, find_case_studies, and check_market_coverage. This provides clear when-to-use and when-not-to-use guidance.

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