Fusión Studio AI — Branding Tools

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

With no annotations, the description carries full burden. It discloses that the tool analyzes website/Instagram and outputs a score with identified leaks, implying a read-only analysis. However, it lacks details on auth needs, rate limits, or what exactly 'brand leaks' entail. Behavioral traits are partially covered.

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 well-structured sentences. The first front-loads the core function and output, the second provides context (target user) and credit. No unnecessary 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?

The description explains the return value (score 1-100 with leaks) but does not specify the format of leaks or how they are presented. It also does not clarify the relationship between required parameter (company_name) and optional ones (website, Instagram) — e.g., whether at least one media source should be provided. This leaves ambiguity for the agent.

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%, but description adds value by linking parameters (website_url, instagram_handle) to the analysis scope ('sitio web y/o Instagram'). It clarifies that these inputs are the sources for the audit, which is not explicit in individual parameter 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?

Description clearly states the tool analyzes brand presence from website/Instagram, returns a score 1-100 with brand leaks, and targets SMEs. Verb is specific and resource is well-defined. While it doesn't explicitly distinguish from siblings, the nature of the tool (audit vs. status/views) is implied.

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?

Description says the tool is useful for evaluating brand identity of any SME, but provides no explicit guidance on when to use this tool over alternatives like brand_book_status or visual_catalog. No when-not-to-use or prerequisite conditions are mentioned.

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 provided, so description carries full burden. Describes a read operation ('Consulta') with no destructive hints, but does not explicitly state idempotency or lack of side effects. Acceptable for a simple query 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 short sentences, front-loaded with key information. No filler words. Every sentence contributes value.

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, but description mentions returned fields (stage, progress, next steps). Lacks details on response format, error handling, or pagination. Adequate for a simple tool but could be more 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 coverage is 100% with clear parameter descriptions. The description adds only 'Busca por email o brand_book_id', which restates the schema without new semantics. Baseline 3.

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 'Consulta' (consult) and resource 'estado de un Brand Book' with specific outputs (stage, progress, next steps). It distinguishes itself from siblings like brand_audit or purchase_brand_book by focusing on status retrieval.

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?

Implies usage for checking status via email or brand_book_id, but lacks explicit when-to-use or when-not-to-use guidance. No alternatives mentioned despite sibling tools existing.

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?

The name 'viewer' suggests a read-only operation, and the description lists data sections but does not explicitly state it is non-destructive or disclose any side effects. With no annotations, this is adequate but not explicit.

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, concise and front-loaded with the main purpose, then provides context. No unnecessary 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?

For a simple viewer with two parameters and no output schema, the description covers the core functionality and data content. It could mention return format or potential errors but is sufficient for an AI agent.

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 value by explaining the content of each section (strategy, visual, verbal) beyond the enum labels, providing context for the section parameter.

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 including strategy, visual identity, and verbal identity. It distinguishes itself from sibling tools like brand_audit, brand_book_status, and generate_proposal by being a viewer.

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 for AI agents needing brand identity context but does not explicitly state when to use this tool versus siblings like brand_audit or visual_catalog. No alternative recommendations are provided.

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 describes the core action (analyzes leaks, generates pitch) but does not disclose side effects, persistence, or authorization needs. It is minimally adequate but lacks depth.

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 with clear front-loading of the main action. No redundant words; each sentence adds value.

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 single-parameter tool with no output schema, the description covers the purpose and prerequisite. However, it lacks details about the output format or what the proposal contains, which would be helpful for completeness.

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 only parameter (audit_id) has 100% schema description coverage. The description adds context by stating the ID comes from brand_audit, which is useful beyond the schema's format and required flag.

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 based on a brand audit, with a specific verb ('Genera') and resource ('propuesta de venta'). It distinguishes from sibling tools like 'brand_audit' which performs the audit 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 states the prerequisite requirement ('Requiere audit_id de brand_audit'), implying it should be used after a brand audit. However, it does not explicitly mention when not to use or provide alternatives, though the sibling tools are clearly different.

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 disclose behavioral traits. It states the tool generates a payment link and the price, but does not mention whether the link expires, whether multiple links can be created, or if a record is created. This is adequate 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 only two sentences, front-loading the action and price, then stating requirements. Every sentence serves a purpose with no unnecessary words, achieving high conciseness.

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 (4 params, no output schema, no annotations), the description covers key aspects: what it does, requirements, and cost. It lacks details about the response format or side effects, but for a payment link generator, this is mostly sufficient to use 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 coverage is 100%, so the baseline is 3. The description adds value by linking audit_id to the brand_audit tool ('Requiere audit_id de brand_audit'), providing semantic context beyond the schema description 'ID del audit'. This helps the agent understand the parameter's origin.

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 MercadoPago payment link to purchase a Brand Book Professional with the specific price ($310,000 ARS). This verb+resource combination is distinct from sibling tools like brand_audit or brand_book_viewer, 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 specifies a prerequisite: 'Requiere audit_id de brand_audit + datos de contacto.' This tells the agent when to use the tool (after obtaining an audit ID) and what inputs are needed, though it does not explicitly exclude use cases or compare with alternative tools.

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 burden. The verb 'consulta' implies read-only, but there is no explicit statement about side effects, idempotence, or safety. The description does not contradict any annotation (there are none), but could be clearer about behavioral traits.

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, efficient sentence that front-loads the key action and object, includes specific counts and categorization, and wastes no 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?

For a simple query tool with no output schema, the description covers the catalog contents and categories but omits the return format, pagination (limit), or behavior when filters are not applied. It is adequate but not exhaustive.

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 having a description. The tool description adds context about categorization (personality and industry) aligning with filter parameters, but does not add new semantic meaning 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 clearly states it queries a curated catalog of typography and color palettes, with specific counts (38 combinations, 60 palettes). It distinguishes from sibling tools focused on brand audit, book status, proposals, etc., leaving no ambiguity about its function.

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?

While the description hints at usage via filtering by personality and industry, it does not explicitly state when to use this tool over siblings like brand_book_viewer or generate_proposal. No alternatives or exclusions are mentioned, leaving the agent to infer context.

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