Fusión Studio AI — Branding Tools

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

With no annotations, the description must disclose operational behavior, but it only states that it analyzes website/Instagram and returns a score. It does not mention required permissions, external calls, data handling, or limitations (e.g., whether a website URL or Instagram handle is mandatory). This is a significant gap for a tool that likely accesses external resources.

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 and concise, with two functional sentences plus a tagline ('Powered by Fusión Studio AI') that adds no operational value. The core purpose and output are clearly stated without excessive detail, earning a high but not perfect score.

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?

No output schema exists, so the description must explain return values—it does mention a score and brand leaks, but lacks detail on the structure (e.g., list, format). It also does not clarify whether both website and Instagram are needed or how missing inputs are handled, leaving context incomplete for a 6-parameter 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?

All 6 parameters are already described in the schema (coverage 100%). The description adds minimal value by mentioning 'sitio web y/o Instagram', which maps to website_url and instagram_handle, but does not elaborate on parameter semantics beyond the schema. 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?

The description states a specific verb ('analiza'), resource ('presencia de marca'), and output ('score 1-100 con fugas de marca'). It clearly distinguishes from sibling tools like brand_book_status or generate_proposal by focusing on brand audit rather than brand book or proposal generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'Útil para evaluar la identidad visual, coherencia y comunicación de cualquier PyME o emprendimiento.' It implies when to use the tool (assessing brand presence) but does not explicitly name alternatives or exclusions, hence not 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose that the tool returns status fields (etapa, progreso, próximos pasos) and that search is by email or brand_book_id, which is useful. However, it does not explicitly state that this is a read-only operation, what happens if both parameters are provided, or if at least one is required. It does not contradict any annotations, but it lacks rich behavioral context.

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

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

The description is a single, well-structured sentence that front-loads the main action ('Consulta el estado') and follows with specifics. It contains no fluff or unnecessary repetition. Every word earns its place.

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

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

For a simple status-check tool with no annotations and no output schema, the description is reasonably complete: it states the output (status fields) and search methods. However, it does not clarify whether at least one search parameter is required (both are optional in the schema) or how it behaves with neither parameter. It also does not reference sibling tools, which could help contextualize its role. Thus, some gaps remain.

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%—both parameters have clear descriptions in the schema. The description adds the 'o' (or) relationship between email and brand_book_id, indicating they are alternative search keys, which is a slight addition. However, the schema already explains the parameters, so the marginal value is limited, aligning with the baseline of 3 for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Consulta el estado de un Brand Book' (check the status of a Brand Book), specifying the resource and the action. It further details what status means: 'etapa, progreso, próximos pasos' (stage, progress, next steps), distinguishing it from sibling tools like brand_book_viewer (which likely views content) and brand_audit (which likely audits quality). This is a specific verb+resource+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 provides implied usage context: you use this tool when you need status information. It mentions search criteria ('Busca por email o brand_book_id') but does not explicitly differentiate when to use this versus brand_book_viewer or brand_audit. No exclusions or alternative tool names are given, so the guidance is implicit rather than explicit.

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. While the name and description imply a read-only retrieval, it does not explicitly state read-only behavior, auth requirements, error handling, or output format. 'Datos completos' is vague about actual response structure. This is a significant gap for a tool with no annotation support.

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

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

The description is a single, front-loaded sentence that conveys the core purpose and content. No wasted words; it is concise and directly 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?

For a simple viewer tool with 2 params and no output schema, the description covers the central content and use case but lacks details on return format, auth, or edge cases. The enum parameter descriptions help, but an agent might not know what to expect in the response body without additional info.

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 baseline is 3. The description adds meaning by detailing what each section includes (e.g., visual includes paleta + tipografía, verbal includes tono, vocabulario, copies), which clarifies the 'section' enum values. This goes beyond the schema's terse 'Sección a consultar.'

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 provides complete Brand Book data (strategy, visual identity, verbal identity), which is a specific resource with a clear scope. It uses an implied verb 'view' consistent with the tool name, but does not explicitly differentiate from sibling tools like brand_audit or visual_catalog.

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: 'Para que un agente IA use la identidad de marca como contexto.' This tells an agent when to invoke it (when brand identity is needed as context). However, it does not mention exclusions or alternative tools, so it lacks explicit when-not guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that Claude Opus analyzes pitfalls and generates a pitch, which explains the internal logic. However, it does not disclose side effects, output format, or any permissions needed. The behavior is partially transparent but not comprehensive.

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 concise, consisting of three short sentences that each add value: the main purpose, the analysis process, and the required input. It is front-loaded with the primary action and avoids unnecessary 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?

Given the tool has one parameter and no output schema, the description provides a complete enough picture: it states the purpose, mentions the analysis, and indicates the output is a pitch with solutions. It could be more explicit about return format or side effects, but for a simple generation tool it is adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers the single parameter audit_id with a description noting it comes from brand_audit. The description repeats this requirement ('Requiere audit_id de brand_audit'), adding no new semantics. With 100% schema coverage, the baseline is 3, and the description does not go beyond 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?

The description clearly states the tool generates a personalized sales proposal ('Genera una propuesta de venta personalizada') based on a Brand Audit. It specifically mentions analyzing 'fugas' (gaps) and producing a pitch with specific solutions. This is a specific verb+resource+output that distinguishes it from sibling tools like brand_audit or visual_catalog.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context by stating it requires an audit_id from brand_audit, implying it should be used after running brand_audit. This gives a prerequisite and places it in a workflow. However, it does not explicitly mention when not to use it or contrast with 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?

With no annotations, the description must disclose behavior. It correctly reveals that the tool generates a payment link (not a charge) and requires audit_id and contact data. However, it does not mention side effects like creating an order or link expiration, leaving some behavioral gaps.

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, focused sentence that delivers the core purpose, price, and prerequisite. It is concise, front-loaded, and contains zero 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 transactional tool with four parameters and no output schema, the description captures the essential context: what it does, the price, and required inputs. A minor gap is not explaining that the payment link is likely used for an immediate purchase flow, but the overall context is sufficient.

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% with each parameter described, so the baseline is 3. The description adds value by relating audit_id to brand_audit, clarifying the expected input source beyond the schema's generic 'ID del audit'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb ('Genera un link de pago de MercadoPago') and a specific resource ('Brand Book Profesional' at a set price). This unambiguously distinguishes it from sibling tools like brand_audit or generate_proposal.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear prerequisite: 'Requiere audit_id de brand_audit + datos de contacto,' indicating the proper context after completing a brand audit. It does not explicitly mention alternatives, but the unique purpose makes the usage intent clear.

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

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

With no annotations provided, the description must disclose behavioral traits, but it only states what the catalog contains. It does not mention the read-only nature explicitly, the default limit of 10 results per category, or how filtering by multiple parameters interacts. This leaves significant gaps in understanding the tool's runtime behavior, similar to the update_drive example.

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

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

The description is a single, information-dense sentence that front-loads the core purpose and key details. It avoids redundancy and fluff, making it an appropriately sized definition.

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 relative simplicity and the absence of an output schema, the description provides an acceptable overview but lacks clarity on the exact return structure (e.g., list vs. grouped results) and default behavior when no parameters are provided. The mention of 38 typography and 60 palettes could be misconstrued as the total result set, whereas the limit parameter defaults to 10 per category.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with descriptions for every parameter, so the baseline is 3. The description reinforces that personality and industry are filtering categories, but it does not add significant new meaning beyond the schema's parameter descriptions, which already explain 'Filtrar por industria' and 'Filtrar por personalidad'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Consulta') and resource ('el catálogo curado de Fusión Studio'), detailing its contents (38 combinaciones tipográficas y 60 paletas de color) and categorization dimensions (personalidad e industria). This distinguishes it from sibling tools like brand_book_viewer and generate_proposal, which focus on viewing or creating specific brand assets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage as a query tool for design catalog content, but it does not explicitly state when to use it over alternatives or provide exclusions. While the purpose makes it clear this is for browsing curated visual options, there is no guidance on scenarios where other sibling tools would be more appropriate, such as when needing to generate a proposal or audit a brand.

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