Server Quality Checklist

Tool Scores

• consultar_rucA
  3.6/5.0
  Behavior3/5

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

  No annotations exist, so the description carries full burden. It discloses the local nature and index prerequisite, but does not mention error handling, rate limits, or behavior for missing RUCs.

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

  Conciseness5/5

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

  The description is three concise sentences. The first front-loads the primary purpose and returned data, the second gives a prerequisite, and the third clarifies privacy. No redundant or irrelevant text.

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

  Completeness4/5

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

  For a simple one-parameter tool with no output schema, the description covers purpose, prerequisite, and local operation. It lacks details on error responses or index requirements, but is sufficient for basic usage.

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

  Parameters3/5

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

  Only one parameter (ruc) with schema description coverage 0%. The description implicitly defines it as a RUC number, adding minimal value beyond the tool name. No format or constraints are specified.

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

  Purpose4/5

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

  The description clearly states the tool consults a RUC locally and lists returned fields (razon social, estado, etc.). The verb 'consultar' and resource 'RUC' are specific. However, it does not explicitly differentiate from siblings like validar_ruc or buscar_razon_social.

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

  Usage Guidelines3/5

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

  The description provides a prerequisite (building index with ingest.py) and notes the query is local. But it lacks explicit guidance on when to use this tool versus alternatives or when not to use it.

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

• buscar_razon_socialA
  4.4/5.0
  Behavior5/5

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

  No annotations are provided, so the description fully bears the burden. It discloses default prefix search using an index (milliseconds), substring search scanning all records (tens of seconds), and case-insensitive comparison. This is thorough behavioral disclosure.

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

  Conciseness5/5

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

  The description is three sentences, efficiently front-loaded with the main purpose followed by key details. Every sentence adds value without redundancy.

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

  Completeness4/5

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

  Given the tool has 2 parameters and no output schema, the description covers the core behavior, performance trade-offs, and case sensitivity. It could mention that the result is a list of taxpayers, but that is implied by the purpose.

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

  Parameters3/5

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

  Schema description coverage is 0% for both parameters. The description explains 'texto' (the search text) but does not mention 'limite' (limit), which has a default of 10 but no explanation. The description partially compensates for the missing schema descriptions.

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

  Purpose5/5

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

  The description clearly states the tool searches for taxpayers by business name (razón social) matching a given text. It distinguishes from sibling tools like validar_ruc (validate RUC) and consultar_ruc (consult RUC), which have different purposes.

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

  Usage Guidelines4/5

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

  The description explains default prefix search (fast) and substring search with '%' (slow, tens of seconds), giving clear context on when to use each mode. It does not explicitly exclude alternatives, but the sibling names indicate they cover other queries.

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

• estado_padronA
  4.4/5.0
  Behavior4/5

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

  Without annotations, the description carries full burden for behavioral disclosure. It enumerates the returned fields (URL, checksum, dates, records), implying a read-only operation. It could explicitly state it has no side effects, but the information provided is sufficient.

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

  Conciseness5/5

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

  The description is two sentences long with no wasted words. Every sentence adds meaning: the first lists what is returned, the second gives the use case. Extremely efficient.

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

  Completeness4/5

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

  Given the tool has no parameters and no output schema, the description adequately covers the purpose and output. It could note that the data is from a local index or mention update frequency, but it remains complete for its simplicity.

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

  Parameters4/5

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

  With zero parameters and 100% schema coverage, the description adds value by explaining the output's purpose and content. It goes beyond the empty schema by detailing what 'procedencia' means, meeting the baseline expectation.

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

  Purpose5/5

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

  The description clearly states the tool returns provenance information (source URL, checksum, dates, record count) of the local padron index, which distinguishes it from sibling tools like validar_ruc that operate on RUC data.

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

  Usage Guidelines4/5

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

  The description explains the tool's purpose is to check data freshness before trusting it, providing clear usage context. However, it does not explicitly state when not to use it or mention alternatives.

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

• validar_rucA
  4.7/5.0
  Behavior5/5

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

  With no annotations, the description fully covers behavioral traits: it is an arithmetic check, offline, and does not query any database. It honestly states limitations (valid here does not mean exists in SUNAT).

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

  Conciseness5/5

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

  The description is two sentences: first explains functionality, second clarifies limitations and usage. Every sentence adds value, no wasted words.

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

  Completeness5/5

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

  Given the single parameter, no output schema, and no annotations, the description is complete. It explains what it does, its limitations, and directs to an alternative. Adequate for a simple validation tool.

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

  Parameters3/5

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

  The schema has 0% coverage and only one parameter 'ruc'. The description mentions '11 digits' but does not explicitly link this to the parameter format expectations. Basic meaning is clear from context, but could be more explicit.

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

  Purpose5/5

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

  The description explicitly states it validates the structure of a Peruvian RUC, specifying 11 digits, valid prefix, and checksum algorithm. It distinguishes from siblings by clarifying it's offline and does not query databases.

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

  Usage Guidelines5/5

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

  The description clearly states when to use this tool (offline structural validation) and when not to (for actual existence, use consultar_ruc). It also notes that a valid result does not guarantee the RUC exists in SUNAT.

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