Server Quality Checklist

Tool Scores

• describe_accessA
  4.2/5.0
  Behavior3/5

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

  No annotations provided. The description implies read-only behavior by saying 'Show' and details what information is returned, but doesn't explicitly state it's a safe read operation or mention any side effects.

  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 concise sentences with the core purpose first, no fluff. Every word adds value.

  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?

  Lists all key elements returned (tables/columns, row cap, timeout, RLS status). Without an output schema, this is sufficient context for a policy inspection tool.

  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?

  No parameters, so baseline 4 applies. The description doesn't need to add parameter info beyond the empty schema.

  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 'Show the effective access policy' and lists specific details (tables/columns, row cap, timeout, RLS). This distinguishes it from sibling tools like list_tables or describe_table.

  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 advises to call before querying to avoid asking for inaccessible data. While it doesn't list alternatives, the use case is clearly scoped.

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

• describe_tableA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden. It discloses the authorization constraint (allowlisted) and the scope of returned info (columns exposed by policy, types, etc.). Does not mention side effects or errors, which are minor given the read-only nature implied by 'describe'.

  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?

  Single sentence, front-loaded with the action, and efficiently lists all return aspects. No redundant words; every piece earns its place.

  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 simple tool (1 param, no output schema), the description is nearly complete. It covers purpose, input constraint, and output content. Minor gap: no mention of error handling for nonexistent tables, but this is acceptable for a straightforward describe tool.

  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?

  Schema coverage is 100%, so baseline is 3. The description adds value by reinforcing that the table must be allowlisted and by providing a concrete example ('public.orders'), which aids correct parameter usage.

  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 uses specific verb 'describe' and resource 'one allowlisted table', listing exactly what metadata is returned (columns, types, nullability, keys, indexes). It clearly distinguishes from siblings like list_tables (listing tables) and run_query (executing SQL).

  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 implicitly indicates use for table schema inspection, but lacks explicit guidance on when not to use or alternatives. However, the context of sibling tools and the specific language about allowlisted tables provides sufficient contextual clarity.

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

• explain_queryA
  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. It explicitly states the tool is read-only, never executes (EXPLAIN, not EXPLAIN ANALYZE), and enforces the same policy gate as run_query. This sufficiently discloses behavioral traits.

  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 extremely concise with two sentences, front-loaded with the core purpose, and contains no unnecessary 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?

  Given the tool's simplicity (1 param, no output schema), the description covers essential aspects: what it does, how it differs from run_query, and policy gate. It could be slightly more complete by mentioning output format, but is adequate.

  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?

  Schema coverage is 100%, so baseline is 3. The description adds value by specifying the sql parameter must be read-only and subject to the same policy as run_query, beyond the schema's minimal 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?

  The description clearly states it shows the query plan for a read-only SELECT without executing it, specifically using EXPLAIN. It distinguishes from siblings like run_query and sample_rows by mentioning 'never EXPLAIN ANALYZE' and 'Same policy gate as run_query'.

  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 provides context by referencing the same policy as run_query, implying it's for read-only analysis. However, it does not explicitly state when not to use it or directly compare with alternatives.

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

• sample_rowsA
  4.4/5.0
  Behavior4/5

  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 read-only nature and filter capabilities, but does not describe return format or default limit behavior, which would enhance transparency.

  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 efficient sentences: first defines purpose and capabilities, second offers usage guidance. No redundant words, front-loaded with key information.

  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 read tool with three parameters and no output schema, the description adequately covers purpose, filters, and sibling differentiation. Missing output format description is a minor gap, but overall complete enough.

  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?

  All three parameters have schema descriptions (100% coverage), so baseline is 3. The tool description adds minimal extra meaning beyond the schema, only reiterating filter simplicity and limiting.

  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 reads rows from one allowlisted table with specific filter operations and a limit, and distinguishes itself from sibling 'run_query' by specifying its use case for straightforward lookups versus joins/aggregations.

  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 when to use this tool ('straightforward lookups') and when to use the alternative 'run_query' (joins and aggregations), providing clear context for selection.

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

• list_tablesA
  4.5/5.0
  Behavior4/5

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

  Without annotations, the description discloses the scope limitation (only visible tables) and the output content (row counts, comments), which is adequate for a read-only list operation.

  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 concise sentences, front-loaded with the main action and followed by a key constraint, with 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?

  The description is complete for a parameterless list tool: it explains what it does, what it returns (estimated row counts, comments), and a critical limitation (visibility under access policy). No output schema needed given the 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, baseline is 4. The description adds value by describing the output (row counts, comments), compensating for the lack of parameters.

  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 tables visible under the access policy with row counts and comments, and distinguishes from sibling tools like describe_table which focuses on a single table.

  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?

  It explains that only tables within the access policy are listed and those outside cannot be queried, providing context for when to use this tool, though it does not explicitly state 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.

• run_queryA
  4.6/5.0
  Behavior5/5

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

  With no annotations provided, the description fully covers behavioral details: parsing with real PostgreSQL grammar, access policy check, read-only transaction, statement timeout, row cap, and error messages that name the denying rule. This is comprehensive for a query execution tool.

  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 concise three-sentence paragraph, front-loaded with the core purpose. Every sentence adds distinct value: purpose, execution details, and error reporting. No unnecessary 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?

  Given the single parameter with full schema coverage and no output schema, the description adequately covers purpose, behavior, and constraints. However, it does not explicitly describe the return format (e.g., rows as JSON), which might be assumed but is not stated.

  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?

  The input schema already provides a detailed description for the 'sql' parameter (100% coverage). The tool description adds context about the execution environment (read-only transaction, timeout, row cap) and error behavior, which supplements the schema without repeating 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 begins with 'Run one read-only SELECT against the allowlisted tables,' which clearly states the action (run), the resource (SELECT query on tables), and the scope (allowlisted, read-only). This distinguishes it from sibling tools like 'list_tables' or 'sample_rows' that handle different operations.

  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 specifies that the tool only accepts read-only SELECT statements and details what is rejected (DML, DDL, multiple statements, etc.). While it does not explicitly name alternative tools for those cases, the constraints make the appropriate usage clear.

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