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

Annotations indicate a mutating operation (readOnlyHint=false), and the description confirms this by stating it applies crop and overlay. It adds valuable context beyond annotations: it explicitly notes no OpenAI call (zero cost), warns about base64 payload size/truncation, and clarifies that overlay is applied after crop. The warning about base64 and the error-handling scenario enrich behavioral understanding.

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-loaded with the core action and input sources, then providing use cases. Zero filler, and the cost/no-OpenAI note is efficient. It covers purpose, usage, and key constraints without 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?

Given 8 parameters (including nested objects) and no output schema, the description is fairly complete: it explains input types, use cases, and the special error scenario. It does not explicitly describe the return format, but parameter names like incluir_base64 and incluir_preview hint at output options. The lack of an output schema is not a gap because the description guides invocation sufficiently.

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% — every parameter has a descriptive set in the input schema. The tool description does not add parameter-level information that isn't already present in the schema (e.g., the order of overlay after crop is noted in the schema's overlay_text description). Per calibration, a baseline of 3 is appropriate when the schema handles parameter documentation fully.

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: 'Aplica crop_to y/o overlay_text' on an existing image, and lists input sources (URL, local path, base64). It clearly distinguishes itself from siblings by emphasizing it works on existing images and does not call OpenAI, setting it apart from image generation 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?

Explicit usage scenarios are given: 'Úsalo para ajustar el texto de un thumbnail sin regenerar, o para recomponer cuando generar_imagen/generar_variacion devolvió tipo_error postproceso'. It also mentions the cost benefit ('costo cero') and references sibling tools by name, providing clear when-to-use and when-not-to-use context.

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?

Goes well beyond the annotations (readOnlyHint=false, openWorldHint=true) by disclosing concrete side effects (saves file to disk), the return contract (file_path), and the failure contract (explicit tipo_error, código, mensaje; never an empty image). This is genuinely useful 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?

Three dense, front-loaded sentences with no filler. Core purpose comes first, followed by side effect/return value and error behavior. 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 complex 13-parameter tool with no output schema, the description covers the primary return path and error behavior, while the rich schema descriptions cover parameter-level details. It could be more explicit about multi-image responses when n>1 or optional base64/preview fields, but it is substantially 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 100%, so the baseline is 3. The description does not add significant per-parameter meaning beyond the schema; 'prompt en inglés' repeats schema info, and file_path/error details describe output, not parameters.

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 action ('Genera una imagen desde cero'), resource ('API de OpenAI'), and outcome (guarda el archivo y devuelve file_path). The phrase 'desde cero' distinguishes it from sibling tools like generar_variacion and componer_imagen.

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?

Provides some context ('para adjuntarla a Pipefy con upload_file_path') but never explicitly says when to use this tool vs alternatives like generar_variacion, componer_imagen, or listar_modelos_imagen. Usage is implied rather than clearly stated.

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?

readOnlyHint=false already signals mutation; the description adds that input can be Pipefy-signed URLs, local paths, data URLs or base64, and that 'mask' performs inpainting. It also anchors the output expectation by saying it 'Devuelve file_path igual que generar_imagen.' No contradiction with annotations.

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 short, information-dense sentences: action first, use case second, input/output constraints last. No filler or repeated schema boilerplate beyond one useful summary of accepted sources.

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 16 parameters and no output schema, the description covers tool selection, input formats, inpainting capability, and the key output field. Rich parameter descriptions supply the rest; only an explicit return-shape listing and clear alternatives would make it fully 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?

All 16 parameters are documented at 100% schema coverage with defaults, enums, and constraints, so the baseline is 3. The prose repeats high-level input kinds and inpainting rather than adding new per-parameter semantics, with only the Pipefy-signed URL note being extra context.

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 and resource: 'Genera una imagen nueva a partir de 1 a 16 imágenes de referencia' and identifies the OpenAI edit endpoint. It separates the tool from 'generar_imagen' by the central role of reference images and even points to the same return contract.

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?

Explicitly says 'Úsalo para derivar las piezas... desde la imagen maestra del Tema', giving a concrete when-to-use scenario. It does not list when-not cases or name alternatives like 'componer_imagen', so it stops short of full routing 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?

Las anotaciones indican readOnlyHint=false y openWorldHint=true, pero la descripción aporta contexto útil: herramienta muta el estado al instalar la fuente, fuente proviene del repo oficial de Google Fonts, y la instalación persiste para uso en overlay_text. No detalla posibles efectos secundarios o requerimientos de permisos, pero con anotaciones presentes el gap es menor.

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?

La descripción es compacta y está bien distribida: propósuta al frente, restricciones de uso, restricciones de fuente y nota de aplicación. Cada oración aporta información necesaria, sin repetir el schema ni las anotaciones.

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?

Para un tool con un solo parámetro y unas pocas reglas de uso, la descripción cubre todos los puntos relevantes: qué hacer, cuándo usar, de dónde sacar la fuente, qué restricciones aplicar y cómo usar el resultado. No exige output schema ni documentación adicional.

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?

El esquema ya tiene 100% de cobertura y define la familia claramente ('Nombre de la familia tal como aparece en fonts.google.com'). La descripción agrega la conexión de que la instalación debe usarse con 'fontFamily', transformando el parámetro en una instrucción aplicable. Esto supera el baseline de 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?

La descripción declara explícitemente la acción (descargar e instalar), el recurso (familia tipográfica del repositorio oficial de Google Fonts) y el contexto de uso (overlay_text). Esto diferencia el tool de los siblings mencionados (generar_imagen, componer_imagen, etc.) sin ambigüedad.

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?

Proporciona reglas claras: usarlo solo cuando el usuario pida explícitamente instalar o usar una fuente, nunca por iniciativa propia ni para cambiar fuentes de marca (Manrope/Inter) sin pedido. Además establece un límite de fuente (solo Google Fonts) e indica qué hacer después de instalar (usar nombre de familia en fontFamily).

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, and the description adds useful behavioral context: it depends on the configured API key and can act as a connectivity check. No contradiction exists between the description and annotations.

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 carry exactly the needed information: the primary purpose and the secondary connection-test use. It is front-loaded and contains 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?

For a zero-parameter, read-only list tool with no output schema, the description covers the main purpose and an additional use case. It could mention the output shape, but the absence is minor given the simplicity of the operation.

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 100% schema description coverage, so the baseline is 4. The description adds no parameter details because none are 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 uses a specific verb ('Lista') and resource ('modelos de imagen de OpenAI') and clarifies the exact scope: models accessible to the configured API key. This clearly distinguishes it from sibling tools like generar_imagen or componer_imagen, which perform different operations.

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 lists accessible image models and also serves as a connection test. It does not explicitly name alternatives or exclusions, but the tool's simple role and distinct siblings make the intended usage obvious.

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 and openWorldHint=false, so the agent knows this is a safe read operation. The description adds that it reads from a 'log local' and groups by model, which is useful context. However, it doesn't disclose details like whether the cost is estimated or actual, or how the log is maintained. With annotations covering the safety profile, a 3 is appropriate.

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, concise sentence that front-loads the main action and resource. It includes the key grouping detail ('agrupado por modelo') without any waste. 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 read-only tool with one optional parameter and no output schema, the description is nearly complete. It tells the agent what the tool does, what it reads, and how it groups results. The only minor gap is that it doesn't describe the return format, but since there's no output schema, a brief note on what the summary looks like could help. However, given the simplicity, this is a minor gap.

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 schema already documents the 'dias' parameter with its range and default. The description doesn't add any additional meaning beyond what the schema provides. Baseline 3 is correct when schema does the heavy lifting.

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: summarizing calls and estimated cost from the local log, grouped by model. It uses a specific verb ('Resume') and resource ('llamadas y el costo estimado... log local'), which distinguishes it from the sibling image-generation tools. However, it doesn't explicitly differentiate from any potential sibling that might also summarize logs, but given the sibling list, it's clear enough.

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 context: it's for reviewing call and cost summaries from the local log. It doesn't explicitly state when to use it vs alternatives, but the sibling tools are all image-related, so the context is clear. No exclusions or alternative conditions are provided, but the tool's purpose is straightforward enough that an agent can infer when to use it.

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