Server Quality Checklist

Tool Scores

• analytics_aggregateA
  4/5.0
  Behavior3/5

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

  Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, establishing safety. The description adds that column/table names are validated against the schema and safe from injection, providing useful behavioral context beyond the annotations. However, it does not elaborate on other behavioral aspects like error handling or performance.

  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, front-loaded with the core purpose. Every sentence adds value: purpose, convenience context, safety note and alternative. No filler or 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?

  With rich annotations, a full output schema, and a simple use case, the description covers the essential aspects: purpose, safety, and alternative. It could mention the output format or that it returns aggregated data, but the output schema likely covers that. Overall it is complete for an AI agent to correctly select and invoke the 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 input schema has 100% description coverage, so the schema fully documents each parameter. The description adds no additional parameter information, meaning it neither enhances nor detracts from the schema's explanations. Baseline of 3 is appropriate.

  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 'Group a table by a column and compute an aggregate — no SQL required,' providing a specific verb and resource. It also implicitly distinguishes from siblings by indicating this is a convenience wrapper for simple aggregations, while 'analytics_run_query' is for more complex queries.

  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 says 'A convenience workflow tool over the most common analytics pattern' and explicitly directs to 'analytics_run_query' for anything more complex, offering clear context and an alternative. It lacks explicit 'when not to use' cases but the guidance is sufficient for an AI agent.

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

• analytics_import_csvA
  4.2/5.0
  Behavior4/5

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

  Adds value beyond annotations by specifying pandas column type inference and requiring valid SQL identifiers. Annotations already mark destructiveHint true, but the description enriches understanding 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.

  Conciseness5/5

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

  Three sentences, front-loaded with core action, each sentence adding critical information without waste. Highly 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?

  Covers key aspects: write action, validation, rule for table name, and if_exists behavior (via schema). Output schema exists, so return values are covered. Slightly more detail on validation process would be ideal, but overall sufficient.

  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 coverage is 100%, so baseline is 3. The description does not add significant new meaning for parameters beyond what's in the schema (e.g., valid SQL identifier is already in schema description).

  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?

  Clearly states 'Load a CSV file into a SQLite table', specifying verb, resource, and validation via pandas. Distinguishes itself from siblings as 'the only WRITE tool', 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.

  Usage Guidelines4/5

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

  Explicitly notes it's the only write tool, providing clear context for when to use it. Does not directly list when not to use alternatives, but this is strongly implied by the write-vs-read distinction.

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

• analytics_run_queryA
  4.4/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: the query runs on a read-only connection with a SQLite authorizer, is validated as a single SELECT/WITH, and results are paginated. This fully discloses behavior beyond annotations.

  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, front-loaded with the core purpose, and 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.

  Completeness4/5

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

  With an output schema present, return values are covered. The description addresses purpose, safety, pagination, and validation. It could mention error handling or result format, but overall it is complete for this 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?

  Schema coverage is 100%, so parameter descriptions are already present. The description mentions pagination via limit/offset but does not add new meaning beyond the schema. Baseline of 3 is appropriate.

  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 'Run a guarded, read-only SQL SELECT query with pagination,' clearly identifying the verb and resource. It distinguishes from sibling tools (like analytics_import_csv or analytics_describe_table) by emphasizing read-only execution and pagination.

  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 that the tool is for read-only queries and validates statements, implying it is safe to run. It does not explicitly list alternatives or when-not-to-use, but the context is clear enough for an agent to choose appropriately.

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

• analytics_describe_tableA
  4.5/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that the tool returns column schema, row count, and sample rows, which gives behavioral context beyond annotations. No contradictions, but the annotations already cover safety.

  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 consists of two sentences: the first defines purpose, the second states usage. No extraneous words, well front-loaded, and efficient.

  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 tool simplicity (3 parameters, 1 required) and the presence of an output schema, the description sufficiently covers what the tool does and when to use it. No gaps.

  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%, so each parameter is documented in the schema. The description does not add significant new meaning beyond reminding to use exact table name from analytics_list_tables. Baseline 3 is appropriate.

  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 shows a table's column schema, total row count, and sample rows. It uses a specific verb-resource combination ('Show a table's column schema...') and distinguishes from siblings by advising to use after analytics_list_tables.

  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 explicitly says to use this tool after analytics_list_tables to understand columns before writing a query. This provides clear context and implicitly guides not to use it for other purposes like running queries or aggregates.

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

• analytics_list_tablesA
  4.7/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context beyond this by specifying it reads only metadata (not row contents) and lists row counts. No contradictions.

  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?

  Two sentences, front-loaded with purpose, then usage guidance. Every sentence serves a purpose; no fluff. Highly efficient.

  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 tool's simplicity (one optional parameter, output schema present, annotations clear), the description fully covers purpose, usage context, and behavioral limitations. No gaps.

  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%, so baseline is 3. The description does not mention the response_format parameter, adding no additional semantic information beyond what the schema provides.

  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 it lists all user tables with row counts, using a specific verb ('List') and resource ('user tables'). It distinguishes from sibling tools like analytics_describe_table (describes specific table) and analytics_run_query (runs arbitrary queries).

  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?

  Explicitly advises 'Use this first to discover what data is available before describing or querying a specific table,' providing clear when-to-use guidance. It also clarifies it does not read row contents, implying when not to use (if row contents are needed).

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