regexcronsql-validator

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses that it uses cron-parser for real calculations (not model guesses), interprets fields in UTC for deterministic results, and costs $0.02 USDC per call. These are meaningful behavioral details, though error handling and edge cases are not 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?

The description is three sentences, front-loaded with the core function, then adds parser details, UTC handling, and cost. Every sentence provides distinct value with no redundancy or fluff, making it highly concise and well-structured.

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 an output schema exists, the description does not need to explain return values. It covers the tool's purpose, calculation method, timezone behavior, and cost, which is quite complete for a simple computation tool. However, it lacks explicit usage guidelines and error behavior, preventing a perfect score.

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 context about UTC interpretation and cron-parser usage, which indirectly informs how parameters like cron_expression and from_date behave, but it does not add explicit parameter-level semantics beyond 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 clearly states the tool calculates the next real executions of a cron expression using cron-parser, which is a specific verb+resource. It distinguishes itself from sibling tools (validar_regex, validar_sql) by focusing on cron expressions and their execution timing, not just regex or SQL validation.

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 by explaining it computes next executions and uses a real parser, but it does not explicitly state when to use this tool vs alternatives or provide exclusions. The UTC timezone note and cost hint at appropriate contexts, but no direct comparison to siblings is given, so guidance is only implied.

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 full transparency burden. It discloses the use of the native JavaScript engine, per-call cost, and the output structure (match, full match, captured groups). However, it does not mention error handling for invalid regex patterns or potential timeouts, which is a minor gap.

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 three sentences long, front-loaded with the main verb 'Ejecuta' and resource. Every sentence adds value: function, reliability disclaimer, and cost. No wasted words, ideal for quick agent parsing.

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 the tool's core purpose, output details, and cost, and an output schema exists to handle return values. It does not explain behavior for invalid regex or edge cases, but that is a minor omission given the tool's simplicity and other structured metadata. Overall, 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?

Schema description coverage is 100%, so the input schema already fully documents all three parameters. The description adds no additional parameter-level detail, merely restating 'patrón' and 'strings de prueba' conceptually. Baseline 3 is appropriate here.

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 ('Ejecuta') and resource ('expresión regular'), and clearly differentiates itself from sibling validators (validar_cron, validar_sql) by focusing on regex execution. It also explains what it returns, making its purpose unambiguous.

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 validating regex against test strings and emphasizes that it uses the real JavaScript engine rather than a model's guess, which is a strong guideline for when to rely on it. It does not explicitly mention alternatives like validar_cron or validar_sql, but the sibling list and regex-specific wording provide sufficient 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?

With no annotations provided, the description provides rich behavioral context: it uses pg-mem (in-memory) with Postgres dialect, tables are created and discarded each call (no persistence), costs $0.04 per call, and returns actual rows. This goes beyond basic function and reveals key operational traits, enabling the agent to anticipate side effects and costs.

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 three concise sentences, each earning its place: function, value proposition (real engine), and behavior (ephemeral tables, cost). It is front-loaded with the core purpose and avoids redundancy. Ideal length for the tool's complexity.

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 moderate complexity, the description covers all key aspects: execution mechanics, engine/dialect, test tables, return value, persistence, and cost. It also benefits from a full output schema (context signal) and complete parameter descriptions in the schema, so the description is sufficient for effective use.

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 input schema already documents both query and tables. The tool description adds minimal semantic value beyond the schema, merely mentioning 'tablas de prueba opcionales' without additional detail. Per the baseline rule for high coverage, this scores a 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 tool's function: 'Ejecuta una consulta SQL (dialecto Postgres, motor pg-mem en memoria) contra tablas de prueba opcionales y devuelve las filas reales resultantes.' It specifies the verb (ejecuta), resource (consulta SQL), and distinct scope (test tables, returns rows). The distinction from siblings ('validar_cron', 'validar_regex') is implicit through the focus on SQL execution, making the purpose unambiguous.

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: 'Usa un motor SQL real, no una suposición del modelo sobre qué devolvería la query.' This signals that the tool is for situations requiring actual SQL execution rather than model inference. It also notes optional test tables and cost, providing practical context. However, it does not explicitly name alternatives or exclusions, so it falls short of a perfect score.

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