Server Quality Checklist

Tool Scores

• inspect_databaseB
  3.3/5.0
  Behavior2/5

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

  With no annotations, the description carries full responsibility for behavioral disclosure. It only states that it returns tables or fields, which is basic. It does not disclose any side effects, permission requirements, error behaviors, or what happens when the table does not exist. For a read-only inspection tool this is relatively benign, but the coverage is minimal.

  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 a single sentence that fully conveys the essential information. It is front-loaded with the primary function and immediately outlines the two actions. No redundancy or filler.

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

  Completeness3/5

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

  Given that an output schema exists, the return format is likely covered elsewhere. The description is sufficient for a simple schema-inspection tool, but it lacks any mention of error handling or the distinction from query_shop. It is adequate but not comprehensive.

  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 input schema already provides 100% coverage: 'action' is described as 'list_tables or describe_table' and 'table' as 'table name for describe_table'. The description repeats this mapping without adding new information, so it meets the baseline but does not exceed it.

  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's purpose: it returns a list of tables or fields of a selected table. This is a specific verb-resource combination and distinguishes it from the sibling query_shop, which presumably queries data rather than schema. The two modes (list_tables, describe_table) are explicitly mentioned.

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

  Usage Guidelines2/5

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

  The description provides instructions on how to use the tool (choose action) but does not explain when to use this tool versus the sibling query_shop. It offers no context about typical scenarios, prerequisites, or exclusions. The usage guidance is limited to internal action selection, not selection among alternatives.

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

• query_shopA
  3.9/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the tool is read-only, that MCP converts queries into safe SQL SELECT, and that canceled orders are excluded from results. This is useful and goes beyond minimal. It does not mention error handling or response format, but the core behavior is well disclosed.

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

  Conciseness4/5

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

  The description is fairly long because it contains eight examples, but those examples are practical and illustrate valid request formats that are directly relevant to the tool. The purpose statement is front-loaded, and each example adds value, so the length is justified. It is well-structured and not overly verbose.

  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 natural-language query tool that has an output schema (indicated by the schema signals), the description covers essential context: what the tool does, how it handles queries, and what data is excluded (canceled orders). It could add more about limitations or error behavior, but given the output schema exists and the description is already thorough, it is largely complete.

  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 100% — every parameter has a clear description. The description itself adds little beyond the schema: it repeats the request examples but does not elaborate on limit or offset semantics, which the schema already covers adequately. The baseline of 3 is appropriate because the schema handles the heavy lifting.

  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 verb and resource: 'performs a read-only analytical query to the online store database' and explains the MCP converts queries to safe SQL SELECT. It is specific about being read-only and analytical, which differentiates it from a generic database tool and gives a clear sense of its function, though it does not explicitly name the sibling tool.

  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?

  Usage is implied through a set of concrete examples that show typical analytical questions (e.g., 'which country has most customers?'). However, the description does not explicitly state when to use this tool versus the sibling 'inspect_database', nor does it provide any exclusions or when-not-to-use guidance. It relies on the reader to infer that this is for read-only analytical queries.

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