Brandize

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 this is a paid purchase, the pricing tier, the checkout-link behavior, the deliverable change after payment, and the mandatory Terms/Privacy acceptance gate. This gives the agent a clear picture of side effects and prerequisites.

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 front-loaded with the core purpose and then covers payment, retrieval, and consent. Every sentence adds necessary information, though 'This is a paid purchase' slightly repeats the pricing/checkout context already given. Still, it is appropriately sized for a high-stakes purchase tool.

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 covers purpose, prerequisites, pricing, consent flow, and post-payment retrieval. It does not explicitly state what the function itself returns (e.g., whether the checkout link is the direct return value), but the overall guidance is sufficient for correct invocation despite no 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: jobToken comes from generate_logo, variationId comes from generate_logo_variations, and acceptedTerms requires a specific user consent interaction. This enriches all three parameters without redundancy.

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 states a specific verb ('Purchase') and resource ('alternate designs from generate_logo_variations'), and explicitly distinguishes it from buying the original logo. This clearly differentiates it from sibling tools like generate_logo and generate_logo_variations.

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 the tool (after generating variations, instead of the original logo), how to retrieve the result afterward (poll get_logo_result with the same jobToken), and the required consent flow including what to do if the user declines. This is explicit, actionable guidance with 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?

With no annotations provided, the description carries full responsibility for disclosing behavior. It explicitly states the message is returned unchanged and that success confirms endpoint reachability, making the tool's no-side-effect nature clear.

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, well-structured sentence that front-loads the core purpose and avoids any redundant detail.

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 echo/health-check tool with a fully documented schema, the description is complete. There is no output schema, but the return value is inherently obvious from the promise to return the message verbatim.

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 fully documents the single `message` parameter with a precise description, so the baseline is 3. The tool description adds no further parameter detail beyond referring to the provided message, but no additional compensation is needed.

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 a health-check tool with a specific behavior: returning the provided message verbatim to confirm the Brandize MCP endpoint is reachable. This distinguishes it sharply from the sibling generation and purchase tools.

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 gives a clear context for use: verifying that the MCP endpoint is reachable. It doesn't explicitly discuss alternatives or exclusions, but no sibling tool serves a similar health-check purpose, so the intended usage is unambiguous.

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 of behavioral disclosure. It explains that the tool is generative, non-mutating, and returns one of three concrete output formats: CSS custom properties, a Tailwind config snippet, or a plain hex list. It also fixes the result size at five colors, adding specificity beyond the input schema.

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 front-loads the primary action and inputs, then efficiently enumerates the three output representations. There is no filler, repetition, or irrelevant context.

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 tool with three parameters, no annotations, and no output schema, the description covers the essential invocation context: what the palette is, how it is derived, how many colors it contains, and what form the result will take. The schema supplies the remaining details like defaults and enums, so nothing needed for correct use 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 input schema already documents all three parameters fully, including enums, defaults, and a hex format example. The description only loosely restates 'base hex color' and 'harmony rule' without adding new semantic detail. With 100% schema coverage, a baseline score 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 opens with a specific verb, 'Generate', and a precise object: 'a 5-color brand palette from a base hex color and a harmony rule.' This clearly states both the tool's function and its scope. It is also easily distinguished from the sibling tools, which focus on logos, meta tags, pricing, and blog search.

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 clearly implies the intended use case: generating a brand palette from a base color and harmony rule. It does not explicitly mention when not to use it or name alternatives, but no sibling tool performs a similar task, so the usage context is unambiguous.

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 behavioral burden, and it does so thoroughly. It discloses that this is a paid purchase, that output is a watermarked low-res preview, that full-resolution files unlock only after payment, that acceptedTerms=true is mandatory, and that an upgrades list should be offered but not forced. There is no contradiction with any annotation because none exist.

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 long, but the length is justified by the number of critical workflow requirements: brief collection, consent, pricing, upgrades, and post-payment retrieval. Key constraints are front-loaded ('Before calling', 'This is a paid purchase') so an agent encounters the most important operational rules first, and there is minimal 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?

Given 11 parameters, no annotations, and no output schema, the description is remarkably complete. It explains the return value (watermarked preview, checkout link, upgrades list), the post-payment retrieval mechanism (poll get_logo_result with jobToken), legal/consent preconditions, and how to handle user uncertainty. An agent has enough context to execute the tool correctly from start to finish.

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 baseline is 3. The description adds real value beyond the schema by explaining the user-interaction workflow: collect the brief before calling, ask for anything not already stated, propose-and-confirm when the user has no preference, and handle acceptedTerms as a multiple-choice consent question. This turns static parameter names into a concrete collection procedure.

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 states a specific action ('Generate a brand logo') and then precisely defines the tool's scope: a paid logo purchase that returns a preview and checkout link, not the full deliverable. It also distinguishes from siblings by pointing to get_logo_result for full-resolution retrieval and get_pricing_tiers for price comparison, and by calling out that variations are part of upgrade tiers rather than this initial generation call.

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 gives explicit, actionable when-to-call conditions: collect a design brief first, never assume defaults, ask about any missing field, propose and confirm options when the user has no preference, offer upgrades before checkout, and obtain explicit Terms/Privacy consent. It also provides a clear when-not case: if the user declines consent, stop and do not generate.

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, and it does so thoroughly. It discloses that results are watermarked/low-res previews, that free credits are consumed and reported, that variationId can be used with buy_logo_variation, and how purchased COMPLETE jobs differ from other purchased tiers. This is far beyond minimal behavioral disclosure.

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 long but every sentence earns its place given the tool's genuinely branching behavior across credit states and purchase tiers. It front-loads the core purpose and then structures the credit, preview, purchase, and post-purchase cases in logical order with no 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?

There is no output schema, so the description correctly explains the return value: a watermarked low-res preview plus remaining credits, with variationId available for purchase. It also covers all relevant usage states and alternatives, leaving no critical gap for an agent deciding whether and how to invoke the 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 description coverage is 100%, so the schema already documents count and jobToken. The description adds meaningful context by tying count to the free-preview credit cap ('capped by the free-preview credits remaining') and explaining how results relate to variationId, which helps an agent reason about the tool without opening 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?

The description opens with a specific verb and resource: 'Generate alternate style directions for an existing generate_logo job.' It clearly distinguishes this tool from generate_logo (which creates the original job) and buy_logo_variation (which purchases a variation), so an agent can tell what it is for.

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 gives explicit usage context: it applies to an existing generate_logo job, is free for preview, and behaves differently before purchase, after a COMPLETE purchase, and on other purchased tiers. It also points to buy_logo_variation for purchasing a generated variation and get_logo_result for retrieving deliverables, spelling out when-not conditions like 'Any other purchased tier refuses outright.'

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 must carry the behavioral disclosure burden. It usefully discloses the output type and that all fields are optional. It does not describe formatting details, handling of empty input, or how fallbacks and defaults apply beyond what the schema already documents.

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 front-loads the verb and object while compactly listing the affected tag types. It contains no redundant phrases or unnecessary repetition of schema details.

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 11 optional parameters and no output schema, the description provides the essential output contract: an HTML head meta-tag block. It could add more detail about exact output formatting or empty-input behavior, but the schema covers parameter semantics, so an agent has enough information 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?

The schema already documents all 11 parameters at 100% coverage, so the description does not need to restate them. It adds only the 'all fields optional' note, which is also implied by the zero required parameters. This meets the baseline for schema-driven 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 uses a specific verb ('Generate') and a clear resource ('an HTML <head> meta-tag block'), then enumerates the tag categories included. This makes the tool's function immediately clear and distinguishes it from the sibling generate_schema_markup, which produces structured data rather than HTML meta tags.

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 'from page details' and the enumeration of tag types make the intended use case evident. However, it does not explicitly name alternatives or state when not to use this tool, so the guidance is clear but lacks explicit exclusions.

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 behavioral burden. It clearly communicates that the tool transforms a flat data object into markup and highlights the FAQPage items format. But it does not explicitly state that the operation is side-effect-free or describe validation/error behavior, leaving some ambiguity.

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, front-loads the main purpose, and uses its second sentence only for a valuable edge case. Every word earns its place; there is no fluff or redundant restatement.

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 generation tool with a rich input schema, the description covers the main call path and the FAQPage exception. It tells the agent what will be produced ('JSON-LD <script> block') and the key input shape. It could slightly improve by noting that the output is returned as a string, but that is largely inferable.

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 schema already documents both the 'type' enum and the 'data' object shape. The FAQPage note in the description essentially repeats what the schema already says, adding no meaningful parameter semantics beyond the structured 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 states a specific verb ('Generate'), a clear resource ('JSON-LD <script> block for a schema.org type'), and even enumerates supported types. This makes it immediately distinguishable from sibling tools like generate_meta_tags, which handle HTML meta tags rather than structured data markup.

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: when JSON-LD schema markup is needed for one of the listed schema.org types. However, it provides no explicit when-not-to-use guidance or alternatives, and the only conditional note is the FAQPage input shape.

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 behavioral burden and does well: declares the operation read-only and safe to poll, explains pending vs paid statuses, and discloses that payment confirmation is not instant. It does not cover failure or timeout behavior, but that is not essential for basic use.

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 action and resource, then the return behavior and polling guidance. Every clause adds useful information; no filler or repetition of schema details.

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 tool is simple with one required parameter, and despite lacking an output schema or annotations, the description covers input provenance, return behavior, and polling cadence. It would be complete for an agent to call it correctly; the only omission is handling of non-paid/non-pending terminal states.

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 only parameter, jobToken, is already described in the schema as the token returned by generate_logo. The description adds little beyond restating that the job is polled by jobToken, 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?

States a specific verb ('Poll'), a resource ('a logo job'), and an identifier ('jobToken'), then explains the two outcomes (paid URL vs pending). This clearly distinguishes it from sibling generation tools like generate_logo and buy_logo_variation.

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 explicit usage context: after payment, poll every ~5 seconds for up to a few minutes until status is paid. It doesn't name alternative tools, but no sibling is a polling alternative, so the lack of an explicit when-not is a minor gap.

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 behavioral burden. It successfully discloses that the operation has no side effects ('Read-only; nothing is purchased') and states what will be returned (price and features). It does not mention authentication, rate limits, or error behavior, but these are minor for such a simple parameterless 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?

A single sentence that front-loads the core purpose, adds the key details (USD price, included features), and then clarifies the read-only behavior. Every word earns its place; no filler or redundancy.

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, side-effect-free list operation with no output schema and no nested objects, the description is complete. It tells the agent exactly what the tool returns (service tiers with price and features) and confirms no purchase side effect, which is everything needed to select and invoke it 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 and schema coverage is 100%, so there is nothing the description needs to clarify about parameters. The baseline for zero-parameter tools is 4, and the description provides no contradictory or missing parameter information.

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 uses a specific verb ('List'), identifies the resource ('Brandize service tiers'), and specifies the exact content returned (price in USD, included features). It also distinguishes itself from purchase-oriented siblings like buy_logo_variation by explicitly noting nothing is purchased.

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 clearly implies this tool is for retrieving pricing/tier information without performing a transaction. It does not explicitly name alternative tools or list exclusions, but the 'Read-only; nothing is purchased' statement provides clear contextual guidance against purchase-related 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 the core behavior: searching by keyword and returning matching post titles, excerpts, and URLs. The search verb also implies a non-mutating operation, though it does not explicitly address result limits or ordering.

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 front-loads the action and resource, then specifies the return payload. There is no filler or redundant 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 simple one-parameter search tool with no output schema, the description is complete: it states the domain, the search mechanism, and the exact return values. Nothing critical is missing for an agent to invoke it 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 query parameter is already well documented in the schema. The description adds 'by keyword,' which reinforces the parameter's meaning but does not significantly extend beyond the schema's own 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 uses a specific verb ('Search') and a clear resource ('Brandize blog posts') with an explicit scope ('logo design and branding guides'). It also states what the tool returns, making it easily distinguishable from the sibling generation and purchase tools.

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 clearly implies when to use the tool: when the user wants to find blog content by keyword. It does not explicitly state when not to use it or name alternatives, but the sibling list contains no other search tool, so the usage context is clear enough.

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