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

The description adds useful edge-case behavior beyond the annotations: an unknown slug returns the list of valid slugs. It also tells the caller that the full markdown write-up is included, which helps with downstream planning. Annotations already declare read-only, idempotent, and non-destructive behavior, so the description does not need to repeat those.

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 compact and front-loaded: output first, then the precondition and edge-case behavior. The 'Read-only.' sentence is redundant with the readOnlyHint annotation and could be dropped, so it is not completely waste-free.

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-only tool with no output schema, the description covers what is returned, what is required, and what happens on bad input. No critical information appears missing for an agent to select 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?

The schema already documents the slug parameter with an example, so the baseline is 3. The description adds meaning beyond the schema by stating that the slug must come from list_case_studies and describing what happens when an invalid slug is passed, which improves correct parameter selection.

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 ('Returns') and target ('one GingerLive campaign case study') and enumerates the returned fields (title, date, URL, markdown write-up). It is clearly distinguished from list_case_studies by requiring a slug and returning a single item.

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 the slug must come from list_case_studies, which orients the agent to call the listing tool first. It also discloses the fallback behavior for an unknown slug, giving the agent a recovery path. It does not spell out when not to use this tool relative to non-listing siblings, 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?

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context beyond those hints: the data is 'a static snapshot of gingerlive.io's public facts,' which tells the agent the result is cached/stable rather than live. It does not explicitly discuss auth or rate limits, but the public static nature makes those omissions minor.

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 with no wasted words. It front-loads the core return value, then immediately provides routing guidance, then closes with a behaviorally relevant note about static snapshot data. 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?

The description is complete for a zero-parameter read-only tool. It explains what the return value contains, when to prefer this tool, and that the data is static and public. No output schema exists, but the description adequately covers the expected return categories.

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 is empty, so the baseline of 4 applies; there are no parameter names or formats for the description to clarify. The description's enumeration of return content is a bonus but not necessary for parameter 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?

The description clearly states the specific verb 'Returns' and the resource: GingerLive's company profile, and enumerates its contents (platforms, network stats, measurement partners, contact links). It also distinguishes itself from sibling tools by naming them explicitly in the usage guidance.

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 use this tool 'first for general what is GingerLive questions' and names alternatives for specific cases: get_network_stats for reach numbers, list_ad_formats for formats, and get_streamer_program_info for the creator side. This leaves no ambiguity about when to choose this tool over its 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds only marginal context beyond that by saying figures are 'GingerLive's published network numbers,' but no additional behavioral caveats such as data freshness or limitations are disclosed.

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 with no redundancy beyond a brief read-only note. It front-loads the return content, then gives usage direction, earning every word.

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 parameterless, read-only tool with no output schema, the description is complete: it lists the returned metrics, states the appropriate use case, names the alternative, and clarifies the data source. Nothing essential for correct invocation 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 has zero parameters, so there are no parameter semantics to document; the baseline is 4. The description usefully explains what data the returned result contains, which is sufficient given the empty 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?

The description names a specific verb and resource: 'Returns GingerLive's network reach and performance figures' and enumerates the exact metrics included. It also differentiates from sibling tool list_case_studies, so an agent can confidently select this 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?

It explicitly states when to use the tool ('when a user asks about scale or performance benchmarks') and names the alternative for campaign-specific results ('use list_case_studies'). This gives the agent clear routing guidance with no inference required.

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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable context beyond those annotations by specifying that this is 'public program information' and read-only, implying no authentication or side effects. It also outlines the return content, which helps set expectations for what the agent will receive.

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 tight sentences: first the core return value, then the usage-routing guidance, then the safety/public note. Every sentence carries distinct information and no words are wasted, with the most important content 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?

For a zero-parameter, read-only informational tool with no output schema, the description fully covers what an agent needs: what the tool returns, when to use it, and which siblings handle other cases. The listed content areas (cost, ad program, platforms, sign-up link) give a clear picture of the expected result without needing a formal output schema.

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, so the baseline for this dimension is 4. The description does not discuss parameters because none exist; instead, it clarifies what the tool returns, which is the only semantic information an invoker needs. No further explanation is required or possible.

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 opens with a specific verb and resource: 'Returns how livestreamers join and earn with GingerLive,' then enumerates the exact content (cost, ad program mechanics, supported platforms, sign-up link). It also explicitly differentiates from siblings by directing brand-side questions to get_company_overview or list_ad_formats, so the tool's scope is 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 states precisely when to use this tool ('Use for creator or streamer monetization questions') and explicitly names alternatives with their condition ('for brand-side questions use get_company_overview or list_ad_formats instead'). This gives an agent a clear routing rule with no inference required.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing output content ('each with a short description, plus format badges such as unskippable and adblock-safe'), which is useful since there is no output schema. It redundantly says 'Read-only,' but no contradiction exists.

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 with no filler. The main action is front-loaded ('Lists the livestream ad formats'), and the examples add useful context without being excessive. Every phrase contributes to understanding or selection.

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 parameterless read-only list tool with no output schema, the description fully covers what it returns (formats with descriptions and badges), when to use it, and the key alternative. Nothing an agent needs to invoke 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?

The tool takes zero parameters and schema coverage is 100%, so the description does not need to explain parameters. The baseline for 0 params is 4, and the description does not add irrelevant parameter information, so a 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 'Lists the livestream ad formats GingerLive sells to brands' and provides concrete examples (picture-in-picture, banners, rich media) that distinguish it from siblings. It also explicitly differentiates from get_company_overview by noting that tool is for the company itself.

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 usage context ('Use when planning or comparing ad formats') and names the alternative for a different need ('use get_company_overview for the company itself'). This provides clear when-to-use and when-not-to-use guidance, leaving no ambiguity.

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 cover read-only, idempotent, and non-destructive behavior. The description adds value by specifying the return payload shape (slug, title, excerpt, URL) and clarifying the relationship to get_case_study. It does not discuss pagination or ordering, but those are minor for a read-only list 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?

The description is short and front-loaded: it states the action and output in the first sentence, then adds usage guidance and a read-only note. No sentence is redundant or 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?

Given zero parameters, rich annotations, and no output schema, the description provides everything needed: purpose, returned fields, when to use it, and the next tool to call. An agent can confidently select and invoke this 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 tool has zero parameters, so the schema coverage is complete and there is nothing for the description to add. The baseline of 4 is appropriate for a no-parameter tool, as parameter ambiguity cannot exist.

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 lists GingerLive campaign case studies and enumerates the returned fields (slug, title, short excerpt, URL). It distinguishes itself from the sibling get_case_study, which is explicitly referenced as the follow-up for full write-ups.

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 explains when to use this tool: to find proof points or results for a brand, category, or format, and provides the concrete next step of calling get_case_study with a slug. This gives the agent both a selection cue and an action pathway.

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