Hacé Cuentas — Free Calculators

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

With no annotations, the description carries the full burden. It states the tool computes and returns JSON, implying a read-only operation. It does not mention side effects, permissions, or destructive potential, but the computation nature suggests safety. The description is transparent enough for most use cases.

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 main action, and every word is useful. It is concise without omitting critical 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?

Given 3 parameters (2 required), 100% schema coverage, no output schema, the description adequately explains the tool's function, parameters, and return type. It also cross-references get_calculator for input structure, completing the context.

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 description adds value by instructing to see get_calculator for inputs structure and clarifying the lang parameter (language of result, default es). This extra context helps the agent construct proper calls.

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 that the tool computes a live calculator result using a slug and inputs object. It distinguishes from siblings by specifying the computation action, while get_calculator retrieves calculator details and search_calculators searches calculators.

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 instructions on what to pass (slug, inputs, optional lang) and references get_calculator for input field structure. It implicitly explains when to use this tool (when computing a result) but does not explicitly discuss when not to use or alternatives beyond the referenced tool.

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?

Despite no annotations, the description adequately explains the tool's output (cards with input details and example URL) without mentioning side effects. It is transparent enough for a read-only information retrieval 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 concise sentences with front-loaded purpose and usage guidance, no wasted 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?

Given a single parameter and no output schema, the description fully covers what the tool returns and its role relative to sibling tools ('compute' and 'search_calculators').

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 has 100% coverage with one parameter, and the description adds an example slug value ('calculadora-imc'), slightly enriching the meaning beyond the schema's generic 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 clearly states the tool returns a calculator record by slug, specifying the type of data included (inputs, example URL). It effectively distinguishes from siblings by focusing on retrieval versus computation or searching.

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 instructs to use this tool before 'compute' to learn which parameters to pass, providing clear context and an alternative use case.

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 search behavior, return of slug, and optional filters. Lacks details on pagination or error behavior, but the core read-only nature is clear without contradiction.

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 concise sentences in Spanish, no fluff. Key information is front-loaded: purpose, output usage, and optional parameters.

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 no output schema, description should detail return format. It only states 'Devolvé el slug' but doesn't specify whether results include additional fields (e.g., calculator names, categories). For a search tool, this leaves ambiguity in the agent's understanding of the response.

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 description adds value by explaining the slug's role in tool chaining and the optional category/limit filters. This enhances understanding beyond the basic schema 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 explicitly states the tool searches calculators by keyword (e.g., 'imc', 'préstamo') and returns a slug for subsequent use with sibling tools 'get_calculator' or 'compute'. It distinguishes itself from siblings by clarifying the output's purpose.

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?

Clear guidance: use to find calculators via keyword, then use returned slug with 'get_calculator' or 'compute'. Optional filters for category and limit are mentioned, providing actionable context for when to use this tool.

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