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

With no annotations provided, the description carries the full burden. It discloses the tool's read-only behavior (describing structure implies no mutation) and specifies what information is returned (columns, types, constraints, primary/foreign keys). However, it doesn't mention potential errors (e.g., if table doesn't exist), performance characteristics, or authentication needs.

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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence earns its place: the first sentence states what it does, and the bullet points provide essential parameter and return value details without redundancy.

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 (2 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, parameters, and return values. However, without an output schema, it could benefit from more detail on the return format (e.g., structure of the output).

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?

The schema description coverage is 0%, so the description must compensate fully. It successfully adds meaning beyond the bare schema by explaining both parameters: 'table_name' as 'Name of the table to describe' and 'schema' as 'Schema name (default: public)'. This clarifies their purpose and the default value.

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 specific action ('Describe the structure of a table') and resource ('table'), distinguishing it from siblings like list_tables (which lists names) or query (which executes queries). It explicitly mentions what gets described: 'columns, types, and constraints'.

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 provides clear context for when to use it (to get table structure details), but doesn't explicitly state when not to use it or name alternatives. For example, it doesn't contrast with describe_view for views or list_constraints for constraint-only listings, though the tool name implies table-specific use.

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 carries full burden. It discloses this is a read operation ('Get') and specifies the return format ('View definition SQL and column list'), which is good behavioral context. However, it doesn't mention potential errors (e.g., if view doesn't exist), permissions needed, or whether this is a lightweight vs. expensive operation.

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 perfectly structured and concise: a clear purpose statement followed by organized Args and Returns sections. Every sentence earns its place, with no wasted words, and information is front-loaded appropriately.

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?

For a 2-parameter read tool with no annotations and no output schema, the description provides good coverage: clear purpose, parameter explanations, and return format. It's mostly complete but could benefit from mentioning error conditions or performance characteristics given the database context.

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?

The description adds meaningful context beyond the 0% schema coverage. It explains that 'view_name' is the 'Name of the view' and 'schema' is 'Schema name (default: public)', providing essential semantic understanding that the bare schema lacks. The only gap is not explaining format expectations for these parameters.

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 purpose with specific verbs ('Get the definition and columns') and resource ('of a view'), distinguishing it from siblings like describe_table (for tables) and list_views (which lists views without details). It precisely communicates what information will be retrieved.

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 context by specifying it's for retrieving view metadata, but doesn't explicitly state when to use this vs. alternatives like describe_table or list_views. It provides clear context about what the tool does but lacks explicit comparison or exclusion guidance.

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 carries the full burden and does well by disclosing critical behavioral traits: it's a data-modifying operation ('modifies data'), includes a caution warning, and specifies an environmental prerequisite. It could improve by mentioning transaction behavior or error handling.

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 efficiently structured with purpose first, warnings and prerequisites clearly highlighted, and parameter/return sections separated. Every sentence adds value with no redundancy.

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?

For a write operation with no annotations and no output schema, the description is quite complete—covering purpose, warnings, prerequisites, parameters, and returns. It could be slightly improved by detailing the return format beyond 'affected row count'.

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 0%, but the description compensates by explaining the 'sql' parameter as 'SQL statement to execute' and specifying it must be a write statement (INSERT, UPDATE, DELETE), adding meaningful context beyond the bare 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's purpose with specific verbs ('execute a write SQL statement') and resource types (INSERT, UPDATE, DELETE), and distinguishes it from sibling tools that are primarily read operations like query, describe_table, etc.

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?

Explicit guidance is provided on when to use ('execute a write SQL statement') and when not to use ('Only available if ALLOW_WRITE_OPERATIONS=true is set'), with clear alternatives implied through sibling tool names like 'query' for read operations.

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 carries the full burden. It discloses key behavioral traits: the tool performs a read operation (EXPLAIN), warns about performance implications of the 'analyze' parameter, and specifies the output format ('JSON format with cost estimates'). However, it lacks details on permissions, rate limits, or error handling.

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 well-structured with clear sections (Args, Returns), uses bullet-like formatting for parameters, and every sentence adds value. It is front-loaded with the core purpose and efficiently conveys necessary details without redundancy.

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 (2 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, parameters, and output format, but lacks information on prerequisites (e.g., database connection), error cases, or example usage, which would enhance completeness.

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?

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains that 'sql' is the 'SQL query to explain' and clarifies that 'analyze' runs the query for real stats with a caution note. This fully compensates for the schema's lack of descriptions.

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 specific action ('Get the execution plan for a SQL query') and resource ('SQL query'), using the technical term 'EXPLAIN' to distinguish it from siblings like 'execute' or 'query'. It precisely defines the tool's function without ambiguity.

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 provides clear context on when to use the 'analyze' parameter ('Use with caution on slow queries'), but does not explicitly differentiate when to use this tool versus alternatives like 'execute' or 'query'. It implies usage for query optimization without naming specific sibling tools.

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?

No annotations are provided, so the description carries the full burden. It states that the tool returns 'Database version, connection info, and settings', which gives some behavioral insight into the output. However, it doesn't disclose critical traits like whether this is a read-only operation, potential performance impacts, authentication needs, or error handling, leaving gaps for a tool that likely accesses system-level data.

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 concise and well-structured, with two sentences that efficiently state the purpose and return values. It's front-loaded with the main action and avoids unnecessary details. However, it could be slightly more polished by integrating the return information into a single sentence, but overall, it's efficient with minimal waste.

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 complexity (likely low, as it retrieves static info) and the lack of annotations and output schema, the description is minimally adequate. It explains what the tool does and what it returns, but for a database tool that might involve sensitive or system-level data, it should ideally mention safety, permissions, or data format to be more complete. Without an output schema, the return description helps, but more context would improve completeness.

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?

The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is given for zero parameters, as there's no need to compensate for missing information, and the description doesn't introduce confusion.

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 purpose with a specific verb ('Get') and resource ('database and connection information'), making it easy to understand what it does. However, it doesn't explicitly differentiate this from sibling tools like 'list_schemas' or 'table_stats', which might also provide database-related information, so it doesn't reach the highest score.

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 provides no guidance on when to use this tool versus alternatives. With many sibling tools available for database operations, there's no indication of whether this is for general metadata, specific configurations, or how it compares to tools like 'list_schemas' or 'query'. This lack of context leaves the agent to guess based on the name alone.

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?

No annotations are provided, so the description carries the full burden. It describes the return format ('List of constraints with type, columns, and references'), which is helpful behavioral context. However, it doesn't mention permissions needed, whether it's read-only, potential rate limits, or error conditions.

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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first explains what the tool does, the second documents parameters, and the third describes the return format.

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?

For a 2-parameter tool with no annotations and no output schema, the description provides good coverage: clear purpose, parameter explanations, and return format. It could be more complete by mentioning permissions or error handling, but it's substantially adequate for the tool's complexity.

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?

With 0% schema description coverage, the description compensates by explaining both parameters: 'table_name: Name of the table' and 'schema: Schema name (default: public)'. It adds meaning beyond the bare schema, though it doesn't elaborate on format requirements or constraints.

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 specific action ('List all constraints') and resource ('for a table'), specifying the constraint types (PK, FK, UNIQUE, CHECK). It distinguishes from siblings like list_tables (lists tables) and list_indexes (lists indexes) by focusing specifically on constraints.

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 when needing constraint information for a specific table, but doesn't explicitly state when to use this tool versus alternatives like describe_table (which might include constraints) or other list_* tools. No guidance on prerequisites or exclusions is provided.

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 carries the full burden of behavioral disclosure. It states what the tool does (listing functions) and the return format, but does not mention any behavioral traits such as permissions required, rate limits, pagination, or error handling. This is a significant gap for a tool with zero annotation coverage.

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 appropriately sized and front-loaded, with the core purpose stated first, followed by structured sections for args and returns. Every sentence adds value, though the formatting with separate sections is slightly verbose but still efficient.

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 low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but lacks depth. It covers the purpose, parameter semantics, and return format, but misses behavioral context like permissions or limitations, which is important for a database query tool with no structured annotations.

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?

The description adds meaningful context for the single parameter 'schema' by specifying it as the schema name with a default value of 'public', which is not covered in the input schema (0% schema description coverage). This compensates well for the lack of schema documentation, though it could include more details like format or constraints.

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 verb 'List' and resource 'functions and procedures in a schema', making the purpose specific and understandable. However, it does not explicitly distinguish this tool from sibling tools like 'list_tables' or 'list_views', which list other database objects, missing an opportunity for full differentiation.

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 retrieving functions in a given schema, but provides no explicit guidance on when to use this tool versus alternatives like 'describe_table' or 'query'. The context is clear but lacks any when/when-not statements or named alternatives, leaving usage somewhat open to interpretation.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a list operation, implying read-only behavior, but doesn't specify if it requires database permissions, how it handles non-existent tables, or if there are rate limits. The description adds minimal behavioral context beyond the basic action, leaving gaps for a tool with no annotation coverage.

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 appropriately sized and well-structured. It starts with a clear purpose statement, followed by organized sections for 'Args' and 'Returns', making it easy to scan. Every sentence adds value without redundancy, and the information is front-loaded for quick understanding.

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 (2 parameters, no annotations, no output schema), the description is adequate but has gaps. It explains parameters and return values, but lacks behavioral details like error handling or performance implications. Without annotations or output schema, it should do more to cover usage context, but it meets a minimum viable level for a read-only list tool.

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?

The description adds significant value beyond the input schema, which has 0% schema description coverage. It explains that 'table_name' is the 'Name of the table' and 'schema' is the 'Schema name (default: public)', clarifying parameter meanings that aren't in the schema. However, it doesn't detail constraints like valid schema names or table naming conventions, slightly limiting its completeness.

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 purpose with a specific verb ('List') and resource ('indexes for a table'), making it easy to understand what it does. It distinguishes from siblings like 'list_tables' or 'list_constraints' by focusing specifically on indexes. However, it doesn't explicitly differentiate from all siblings (e.g., 'describe_table' might also provide index information), keeping it from a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'list_indexes' over 'describe_table' (which might include index details) or 'list_constraints' (which might overlap with unique indexes). There's no context about prerequisites, such as needing table existence or specific permissions.

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 carries the full burden of behavioral disclosure. It states the tool lists schemas and describes the return format (name and owner), which adds useful context beyond basic functionality. However, it lacks details on permissions, rate limits, or error handling, leaving gaps for a tool with no annotation support.

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 front-loaded with the core purpose in the first sentence, followed by a concise explanation of returns. Every sentence earns its place by providing essential information without redundancy, making it highly efficient 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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is complete enough for a read-only listing operation. It covers purpose and return format adequately. However, it could improve by addressing potential limitations or linking to sibling tools for broader context.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate, but it compensates by explaining the return values, enhancing understanding of the tool's output semantics.

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 specific action ('List all schemas') and resource ('in the PostgreSQL database'), distinguishing it from sibling tools like list_tables or list_views by focusing on schemas. It provides a precise verb+resource combination that leaves no ambiguity about its function.

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 retrieving schema-level information but does not explicitly state when to use this tool versus alternatives like get_database_info (which might include schema details) or other list_* tools. No guidance is provided on exclusions or prerequisites, leaving usage context inferred rather than stated.

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 carries the full burden of behavioral disclosure. It mentions the return format ('List of tables with name and type'), which adds some context, but lacks details on permissions, pagination, error handling, or performance characteristics. This is a significant gap for a tool with zero annotation coverage.

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 appropriately sized and front-loaded, with a clear purpose statement followed by structured sections for Args and Returns. Every sentence earns its place, providing essential information without waste.

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 low complexity and lack of annotations or output schema, the description is moderately complete. It covers the purpose, parameter, and return format, but lacks behavioral details like side effects or error conditions, which are important for a read operation in a database context.

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?

The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains the parameter's purpose ('Schema name to list tables from'), provides a default value ('default: public'), and clarifies the return semantics, compensating fully for the schema's lack of documentation.

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 specific action ('List all tables') and resource ('in a specific schema'), distinguishing it from siblings like list_views, list_functions, or list_schemas. It precisely defines the scope and target, avoiding vagueness.

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 context by specifying 'in a specific schema' and providing a default value, which helps differentiate from broader tools like get_database_info. However, it does not explicitly state when not to use it or name alternatives, such as list_views for view-specific listings.

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 carries the full burden of behavioral disclosure. It states the action ('List all views') and return format ('List of views with name'), but lacks details on permissions, pagination, error handling, or whether it's read-only. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 appropriately sized and front-loaded, with the core purpose stated first followed by structured Args and Returns sections. Every sentence earns its place without redundancy, making it efficient and easy to parse.

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 low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and return format, yet lacks usage guidelines and detailed behavioral context. For a simple list tool, it's adequate but not fully comprehensive.

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?

The description adds minimal semantics beyond the input schema, which has 0% description coverage. It explains the 'schema' parameter as 'Schema name (default: public)', matching the schema's default but not providing additional context like valid values or examples. With one parameter and low schema coverage, it partially compensates but remains basic.

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 verb ('List') and resource ('views in a schema'), making the purpose specific and understandable. It distinguishes from siblings like list_tables or list_functions by focusing on views, though it doesn't explicitly contrast them. The description avoids tautology by not just restating the tool name.

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?

No guidance is provided on when to use this tool versus alternatives. While it implicitly targets views, it doesn't mention when to choose list_views over other list_* tools or how it relates to describe_view. There's no context on prerequisites or exclusions, leaving usage unclear.

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 carries the full burden of behavioral disclosure. It effectively communicates that the tool is read-only ('READ-ONLY by default'), specifies the type of SQL allowed ('SELECT statements only'), and describes the return format ('rows, columns, and metadata'). However, it doesn't mention potential limitations like query timeout, result size limits, or authentication requirements.

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 perfectly structured and concise: a clear purpose statement, important behavioral context, and parameter/return documentation in separate labeled sections. Every sentence adds value with zero wasted words, making it easy to scan and understand quickly.

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?

For a single-parameter read-only query tool with no annotations or output schema, the description provides excellent context: purpose, usage guidelines, behavioral constraints, parameter meaning, and return format. The main gap is lack of output schema documentation, but the description compensates well by describing return values. Slightly more detail on potential limitations would make it 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?

With 0% schema description coverage for the single parameter, the description compensates by explaining the 'sql' parameter meaning ('SQL query to execute') and adding the critical constraint 'SELECT statements only' that isn't in the schema. This provides essential semantic context beyond the bare schema, though it could specify format expectations or examples.

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 purpose with specific verb ('Execute') and resource ('SQL query against the PostgreSQL database'), distinguishing it from sibling tools like 'execute' for write operations and 'explain_query' for analysis. It precisely defines what the tool does without being vague or tautological.

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 provides explicit guidance on when to use this tool vs alternatives: 'READ-ONLY by default' and 'Use the 'execute' tool for write operations.' It clearly distinguishes between read (SELECT) and write operations, naming the specific alternative tool for different use cases.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the search is 'case-insensitive' and returns a 'List of matching columns with table information', which adds some context. However, it lacks details on permissions, rate limits, error handling, or whether the search is real-time or cached, leaving gaps for a mutation-like operation (searching across databases).

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for 'Args' and 'Returns' that are concise and informative. Every sentence earns its place without redundancy, making it easy to scan and understand quickly.

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 (searching across tables with 2 parameters), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers the purpose and parameters well but lacks behavioral details like error cases or performance implications. The absence of an output schema means the description should ideally explain return values more thoroughly, which it does partially but not fully.

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?

The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'search_term' is a 'Column name pattern to search (case-insensitive)' and 'schema' is 'Optional schema to limit search (default: all user schemas)', clarifying usage and default behavior. This compensates well for the low schema coverage, though it doesn't detail pattern syntax (e.g., wildcards).

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 purpose: 'Search for columns by name across all tables.' This specifies the verb ('search'), resource ('columns'), and scope ('across all tables'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'list_tables', which is why it doesn't reach a score of 5.

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 mentioning 'across all tables' and the optional 'schema' parameter to limit the search, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'describe_table' or 'list_tables'. There's no mention of prerequisites, exclusions, or specific scenarios where this tool is preferred over others.

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 carries the full burden. It clearly describes the read-only nature ('Get statistics') and specifies what information is returned, but lacks details on behavioral aspects like error handling, performance implications, or whether it requires specific permissions.

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 well-structured and appropriately sized. It front-loads the core purpose, then clearly lists arguments and returns in separate sections. Every sentence adds value with no redundant information.

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 (2 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers the purpose and parameters well, but lacks information about return format details (e.g., structure of 'vacuum info') and doesn't address potential limitations or error cases.

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?

The description adds significant value beyond the input schema, which has 0% description coverage. It explains both parameters: 'table_name' as 'Name of the table' and 'schema' as 'Schema name (default: public)', including the default value. This compensates well for the schema's lack of descriptions.

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 purpose with a specific verb ('Get') and resource ('statistics for a table'), and lists the specific statistics returned (row count, size, bloat). However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'list_tables', which might also provide table information.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'describe_table' (which might provide metadata) or 'list_tables' (which might list tables without statistics), leaving the agent to infer usage context.

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